Studying Summarization Evaluation Metrics in the Appropriate Scoring Range

Abstract: In summarization, automatic evaluation metrics are usually compared based on their ability to correlate with human judgments. Unfortunately, the few existing human judgment datasets have been created as by-products of the manual evaluations performed during the DUC/TAC shared tasks. However, modern systems are typically better than the best systems submitted at the time of these shared tasks. We show that, surprisingly, evaluation metrics which behave similarly on these datasets (average-scoring range) strongly disagree in the higher-scoring range in which current systems now operate. It is problematic because metrics disagree yet we can't decide which one to trust. This is a call for collecting human judgments for high-scoring summaries as this would resolve the debate over which metrics to trust. This would also be greatly beneficial to further improve summarization systems and metrics alike.

summarization
evaluation metric
higher-scoring range


Joint Inference for Knowledge Extraction from Biomedical Literature

Abstract: Knowledge extraction from online repositories such as PubMed holds the promise of dramatically speeding up biomedical research and drug design. After initially focusing on recognizing proteins and binary interactions, the community has recently shifted their attention to the more ambitious task of recognizing complex, nested event structures. State-of-the-art systems use a pipeline architecture in which the candidate events are identified first, and subsequently the arguments. This fails to leverage joint inference among events and arguments for mutual disambiguation. Some joint approaches have been proposed, but they still lag much behind in accuracy. In this paper, we present the first joint approach for bio-event extraction that obtains state-of-the-art results. Our system is based on Markov logic and adopts a novel formulation by jointly predicting events and arguments, as well as individual dependency edges that compose the argument paths. On the BioNLP'09 Shared Task dataset, it reduced F1 errors by more than 10% compared to the previous best joint approach.

knowledge extraction
markov logic
joint inference
event extraction
mlns


Representations of language in a model of visually grounded speech signal

Abstract: We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to model the temporal nature of spoken speech, and show that it learns to extract both form and meaning-based linguistic knowledge from the input signal. We carry out an in-depth analysis of the representations used by different components of the trained model and show that encoding of semantic aspects tends to become richer as we go up the hierarchy of layers, whereas encoding of form-related aspects of the language input tends to initially increase and then plateau or decrease.

speech signal
image
semantic information


Combining Language and Vision with a Multimodal Skip-gram Model

Abstract: We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based word representations by learning to predict linguistic contexts in text corpora. However, for a restricted set of words, the models are also exposed to visual representations of the objects they denote (extracted from natural images), and must predict linguistic and visual features jointly. The MMSKIP-GRAM models achieve good performance on a variety of semantic benchmarks. Moreover, since they propagate visual information to all words, we use them to improve image labeling and retrieval in the zero-shot setup, where the test concepts are never seen during model training. Finally, the MMSKIP-GRAM models discover intriguing visual properties of abstract words, paving the way to realistic implementations of embodied theories of meaning.

vision
multimodal skip-gram model
word embedding
fusion approach


Translating Videos to Natural Language Using Deep Recurrent Neural Networks

Abstract: Solving the visual symbol grounding problem has long been a goal of artificial intelligence. The field appears to be advancing closer to this goal with recent breakthroughs in deep learning for natural language grounding in static images. In this paper, we propose to translate videos directly to sentences using a unified deep neural network with both convolutional and recurrent structure. Described video datasets are scarce, and most existing methods have been applied to toy domains with a small vocabulary of possible words. By transferring knowledge from 1.2M+ images with category labels and 100,000+ images with captions, our method is able to create sentence descriptions of open-domain videos with large vocabularies. We compare our approach with recent work using language generation metrics, subject, verb, and object prediction accuracy, and a human evaluation.

video
image captioning
cnn-rnn encoder-decoder framework


Deep Multilingual Correlation for Improved Word Embeddings

Abstract: Word embeddings have been found useful for many NLP tasks, including part-of-speech tagging, named entity recognition, and parsing. Adding multilingual context when learning embeddings can improve their quality, for example via canonical correlation analysis (CCA) on embeddingsfromtwo languages. In this paper, we extend this idea to learn deep non-linear transformations of word embeddings of the two languages, using the recently proposed deep canonical correlation analysis. The resulting embeddings, when evaluated on multiple word and bigram similarity tasks, consistently improve over monolingual embeddings and over embeddings transformed with linear CCA.

multilingual context
canonical correlation analysis
semantic space


Bootstrapping Multilingual AMR with Contextual Word Alignments

Abstract: We develop high performance multilingual Abstract Meaning Representation (AMR) systems by projecting English AMR annotations to other languages with weak supervision. We achieve this goal by bootstrapping transformer-based multilingual word embeddings, in particular those from cross-lingual RoBERTa (XLM-R large). We develop a novel technique for foreign-text-to-English AMR alignment, using the contextual word alignment between English and foreign language tokens. This word alignment is weakly supervised and relies on the contextualized XLM-R word embeddings. We achieve a highly competitive performance that surpasses the best published results for German, Italian, Spanish and Chinese.

amr
word alignment
annotation projection


The CMU-Avenue French-English Translation System

Abstract: This paper describes the French-English translation system developed by the Avenue research group at Carnegie Mellon University for the Seventh Workshop on Statistical Machine Translation (NAACL WMT12). We present a method for training data selection, a description of our hierarchical phrase-based translation system, and a discussion of the impact of data size on best practice for system building.

french-english translation system
word alignment
qe-clean system


CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning

Abstract: Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains. Existing approaches only learn class-specific semantic features and intermediate representations from source domains. This affects generalizability to unseen target domains, resulting in suboptimal performances. To this end, we present CONTaiNER, a novel contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. Instead of optimizing class-specific attributes, CONTaiNER optimizes a generalized objective of differentiating between token categories based on their Gaussian-distributed embeddings. This effectively alleviates overfitting issues originating from training domains. Our experiments in several traditional test domains (OntoNotes, CoNLL'03, WNUT `17, GUM) and a new large scale Few-Shot NER dataset (Few-NERD) demonstrate that on average, CONTaiNER outperforms previous methods by 3%-13% absolute F1 points while showing consistent performance trends, even in challenging scenarios where previous approaches could not achieve appreciable performance.

entity recognition
contrastive learning
few-shot ner


An Unsupervised Model for Text Message Normalization

Abstract: Cell phone text messaging users express themselves briefly and colloquially using a variety of creative forms. We analyze a sample of creative, non-standard text message word forms to determine frequent word formation processes in texting language. Drawing on these observations, we construct an unsupervised noisy-channel model for text message normalization. On a test set of 303 text message forms that differ from their standard form, our model achieves 59% accuracy, which is on par with the best supervised results reported on this dataset.

text message normalization
word formation process
noisy channel model
probabilistic model
variation


CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality

Abstract: Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations. However, the unified annotations do not always reflect the independent sentiment of single modalities and limit the model to capture the difference between modalities. In this paper, we introduce a Chinese single- and multi-modal sentiment analysis dataset, CH-SIMS, which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis. Furthermore, we propose a multi-task learning framework based on late fusion as the baseline. Extensive experiments on the CH-SIMS show that our methods achieve state-of-the-art performance and learn more distinctive unimodal representations. The full dataset and codes are available for use at <https://github.com/thuiar/MMSA>.

multimodal sentiment analysis
unimodal annotation
ch-sims


One-Shot Relational Learning for Knowledge Graphs

Abstract: Knowledge graphs (KG) are the key components of various natural language processing applications. To further expand KGs' coverage, previous studies on knowledge graph completion usually require a large number of positive examples for each relation. However, we observe long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.

relational learning
knowledge graph
triple
graph structure
one-shot learning


Injecting Numerical Reasoning Skills into Language Models

Abstract: Large pre-trained language models (LMs) are known to encode substantial amounts of linguistic information. However, high-level reasoning skills, such as numerical reasoning, are difficult to learn from a language-modeling objective only. Consequently, existing models for numerical reasoning have used specialized architectures with limited flexibility. In this work, we show that numerical reasoning is amenable to automatic data generation, and thus one can inject this skill into pre-trained LMs, by generating large amounts of data, and training in a multi-task setup. We show that pre-training our model, GenBERT, on this data, dramatically improves performance on DROP (49.3 – 72.3 F1), reaching performance that matches state-of-the-art models of comparable size, while using a simple and general-purpose encoder-decoder architecture. Moreover, GenBERT generalizes well to math word problem datasets, while maintaining high performance on standard RC tasks. Our approach provides a general recipe for injecting skills into large pre-trained LMs, whenever the skill is amenable to automatic data augmentation.

numerical reasoning
skill
language model
large amount
drop


Farasa: A New Fast and Accurate Arabic Word Segmenter

Abstract: In this paper, we present Farasa (meaning insight in Arabic), which is a fast and accurate Arabic segmenter. Segmentation involves breaking Arabic words into their constituent clitics. Our approach is based on SVMrank using linear kernels. The features that we utilized account for: likelihood of stems, prefixes, suffixes, and their combination; presence in lexicons containing valid stems and named entities; and underlying stem templates. Farasa outperforms or equalizes state-of-the-art Arabic segmenters, namely QATARA and MADAMIRA. Meanwhile, Farasa is nearly one order of magnitude faster than QATARA and two orders of magnitude faster than MADAMIRA. The segmenter should be able to process one billion words in less than 5 hours. Farasa is written entirely in native Java, with no external dependencies, and is open-source.


Improved chunk-level reordering for statistical machine translation

Abstract: Inspired by previous chunk-level reordering approaches to statistical machine translation, this paper presents two methods to improve the reordering at the chunk level. By introducing a new lattice weighting factor and by reordering the training source data, an improvement is reported on TER and BLEU. Compared to the previous chunklevel reordering approach, the BLEU score improves 1.4% absolutely. The translation results are reported on IWSLT Chinese-English task.

statistical machine translation
chunk
option
psmt system
discrepancy


Solving Geometry Problems: Combining Text and Diagram Interpretation

Abstract: This paper introduces GEOS, the first automated system to solve unaltered SAT geometry questions by combining text understanding and diagram interpretation. We model the problem of understanding geometry questions as submodular optimization, and identify a formal problem description likely to be compatible with both the question text and diagram. GEOS then feeds the description to a geometric solver that attempts to determine the correct answer. In our experiments, GEOS achieves a 49% score on official SAT questions, and a score of 61% on practice questions. 1 Finally, we show that by integrating textual and visual information, GEOS boosts the accuracy of dependency and semantic parsing of the question text.

geometry problem
diagram interpretation
geos
reasoning


Surprising Parser Actions and Reading Difficulty

Abstract: An incremental dependency parser's probability model is entered as a predictor in a linear mixed-effects model of German readers' eye-fixation durations. This dependency-based predictor improves a baseline that takes into account word length, n-gram probability, and Cloze predictability that are typically applied in models of human reading. This improvement obtains even when the dependency parser explores a tiny fraction of its search space, as suggested by narrow-beam accounts of human sentence processing such as Garden Path theory.


Domain-specific vs. Uniform Modeling for Coreference Resolution

Abstract: Several corpora annotated for coreference have been made available in the past decade. These resources differ with respect to their size and the underlying structure: the number of domains and their similarity. Our study compares domain-specific models, learned from small heterogeneous subsets of the investigated corpora, against uniform models, that utilize all the available data. We show that for knowledge-poor baseline systems, domain-specific and uniform modeling yield same results. Systems, relying on large amounts of linguistic knowledge, however, exhibit differences in their performance: with all the designed features in use, domain-specific models suffer from over-fitting, whereas with pre-selected feature sets they tend to outperform union models.

uniform modeling
coreference resolution
domain adaptation


Unsupervised Dependency Parsing with Transferring Distribution via Parallel Guidance and Entropy Regularization

Abstract: We present a novel approach for inducing unsupervised dependency parsers for languages that have no labeled training data, but have translated text in a resourcerich language. We train probabilistic parsing models for resource-poor languages by transferring cross-lingual knowledge from resource-rich language with entropy regularization. Our method can be used as a purely monolingual dependency parser, requiring no human translations for the test data, thus making it applicable to a wide range of resource-poor languages. We perform experiments on three Data sets — Version 1.0 and version 2.0 of Google Universal Dependency Treebanks and Treebanks from CoNLL shared-tasks, across ten languages. We obtain stateof-the art performance of all the three data sets when compared with previously studied unsupervised and projected parsing systems.

entropy regularization
dependency parser
resource-rich language
parallel data
annotation projection


RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models

Abstract: Text representation models are prone to exhibit a range of societal biases, reflecting the non-controlled and biased nature of the underlying pretraining data, which consequently leads to severe ethical issues and even bias amplification. Recent work has predominantly focused on measuring and mitigating bias in pretrained language models. Surprisingly, the landscape of bias measurements and mitigation resources and methods for conversational language models is still very scarce: it is limited to only a few types of bias, artificially constructed resources, and completely ignores the impact that debiasing methods may have on the final perfor mance in dialog tasks, e.g., conversational response generation. In this work, we present REDDITBIAS, the first conversational data set grounded in the actual human conversations from Reddit, allowing for bias measurement and mitigation across four important bias dimensions: gender,race,religion, and queerness. Further, we develop an evaluation framework which simultaneously 1)measures bias on the developed REDDITBIAS resource, and 2)evaluates model capability in dialog tasks after model debiasing. We use the evaluation framework to benchmark the widely used conversational DialoGPT model along with the adaptations of four debiasing methods. Our results indicate that DialoGPT is biased with respect to religious groups and that some debiasing techniques can remove this bias while preserving downstream task performance.

language model
conversational data
redditbias


Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme

Abstract: The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts. Most recent studies are based on the likelihood of Cartesian product among all clause candidates, resulting in a high computational cost. Targeting this issue, we regard the task as a sequence labeling problem and propose a novel tagging scheme with coding the distance between linked components into the tags, so that emotions and the corresponding causes can be extracted simultaneously. Accordingly, an end-to-end model is presented to process the input texts from left to right, always with linear time complexity, leading to a speed up. Experimental results show that our proposed model achieves the best performance, outperforming the state-of-the-art method by 2.26% (p0.001) in F1 measure.


Directed Acyclic Graph Network for Conversational Emotion Recognition

Abstract: The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.

acyclic graph
conversation
graph-based neural model


Multi-Source Cross-Lingual Model Transfer: Learning What to Share

Abstract: Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP tasks. Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language by leveraging labeled data from other (source) languages. In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance. Unlike most existing methods that rely only on language-invariant features for CLTL, our approach coherently utilizes both language-invariant and language-specific features at instance level. Our model leverages adversarial networks to learn language-invariant features, and mixture-of-experts models to dynamically exploit the similarity between the target language and each individual source language. This enables our model to learn effectively what to share between various languages in the multilingual setup. Moreover, when coupled with unsupervised multilingual embeddings, our model can operate in a zero-resource setting where neither target language training data nor cross-lingual resources are available. Our model achieves significant performance gains over prior art, as shown in an extensive set of experiments over multiple text classification and sequence tagging tasks including a large-scale industry dataset.

various nlp task
cross-lingual transfer learning
source language
language-invariant feature
mixture-of-expert model


Cross-Genre and Cross-Domain Detection of Semantic Uncertainty

Abstract: Uncertainty is an important linguistic phenomenon that is relevant in various Natural Language Processing applications, in diverse genres from medical to community generated, newswire or scientific discourse, and domains from science to humanities. The semantic uncertainty of a proposition can be identified in most cases by using a finite dictionary (i.e., lexical cues) and the key steps of uncertainty detection in an application include the steps of locating the (genre- and domain-specific) lexical cues, disambiguating them, and linking them with the units of interest for the particular application (e.g., identified events in information extraction). In this study, we focus on the genre and domain differences of the context-dependent semantic uncertainty cue recognition task.We introduce a unified subcategorization of semantic uncertainty as different domain applications can apply different uncertainty categories. Based on this categorization, we normalized the annotation of three corpora and present results with a state-of-the-art uncertainty cue recognition model for four fine-grained categories of semantic uncertainty.Our results reveal the domain and genre dependence of the problem; nevertheless, we also show that even a distant source domain data set can contribute to the recognition and disambiguation of uncertainty cues, efficiently reducing the annotation costs needed to cover a new domain. Thus, the unified subcategorization and domain adaptation for training the models offer an efficient solution for cross-domain and cross-genre semantic uncertainty recognition.

detection
semantic uncertainty
unified subcategorization
wikipedia article


Biasing Attention-Based Recurrent Neural Networks Using External Alignment Information

Abstract: This work explores extending attention-based neural models to include alignment information as input. We modify the attention component to have dependence on the current source position. The attention model is then used as a lexical model together with an additional alignment model to generate translation. The attention model is trained using external alignment information, and it is applied in decoding by performing beam search over the lexical and alignment hypotheses. The alignment model is used to score these alignment candidates. We demonstrate that the attention layer is capable of using the alignment information to improve over the baseline attention model that uses no such alignments. Our experiments are performed on two tasks: WMT 2016 English → Romanian and WMT 2017 German → English.

external alignment information
attention component
neural machine translation


N-gram-based Machine Translation

Abstract: This article describes in detail an n-gram approach to statistical machine translation. This approach consists of a log-linear combination of a translation model based on n-grams of bilingual units, which are referred to as tuples, along with four specific feature functions. Translation performance, which happens to be in the state of the art, is demonstrated with Spanish-to-English and English-to-Spanish translations of the European Parliament Plenary Sessions (EPPS).

machine translation
bilingual unit
tuple
n-gram-based approach
smt framework


A Survey of the State of Explainable AI for Natural Language Processing

Abstract: Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an overview of the current state of Explainable AI (XAI), considered within the domain of Natural Language Processing (NLP). We discuss the main categorization of explanations, as well as the various ways explanations can be arrived at and visualized. We detail the operations and explainability techniques currently available for generating explanations for NLP model predictions, to serve as a resource for model developers in the community. Finally, we point out the current gaps and encourage directions for future work in this important research area.

survey
explanation method
model output
xai method


Syntactic preprocessing for statistical machine translation

Abstract: We describe an approach to automatic source-language syntactic preprocessing in the context of Arabic-English phrase-based machine translation. Source-language labeled dependencies, that are word aligned with target language words in a parallel corpus, are used to automatically extract syntactic reordering rules in the same spirit of Xia and McCord (2004) and Zhang et al. (2007). The extracted rules are used to reorder the source-language side of the training and test data. Our results show that when using monotonic decoding and translations for unigram source-language phrases only, source-language reordering gives very signiﬁcant gains over no reordering (25% relative increase in BLEU score). With decoder distortion turned on and with access to all phrase translations, the differences in BLEU scores are diminished. However, an analysis of sentence-level BLEU scores shows reordering outperforms no-reordering in over 40% of the sentences. These results suggest that the approach holds big promise but much more work on Arabic parsing may be needed.

parallel corpus
side
syntactic preprocessing
other language
parse quality


Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors

Abstract: The development of compositional distributional models of semantics reconciling the empirical aspects of distributional semantics with the compositional aspects of formal semantics is a popular topic in the contemporary literature. This paper seeks to bring this reconciliation one step further by showing how the mathematical constructs commonly used in compositional distributional models, such as tensors and matrices, can be used to simulate di erent aspects of predicate logic. This paper discusses how the canonical isomorphism between tensors and multilinear maps can be exploited to simulate a full-blown quantifier-free predicate calculus using tensors. It provides tensor interpretations of the set of logical connectives required to model propositional calculi. It suggests a variant of these tensor calculi capable of modelling quantifiers, using few non-linear operations. It finally discusses the relation between these variants, and how this relation should constitute the subject of future work.

logical calculi
distributional model
tensor calculus


Semantic Frames to Predict Stock Price Movement

Abstract: Semantic frames are a rich linguistic resource. There has been much work on semantic frame parsers, but less that applies them to general NLP problems. We address a task to predict change in stock price from financial news. Semantic frames help to generalize from specific sentences to scenarios, and to detect the (positive or negative) roles of specific companies. We introduce a novel tree representation, and use it to train predictive models with tree kernels using support vector machines. Our experiments test multiple text representations on two binary classification tasks, change of price and polarity. Experiments show that features derived from semantic frame parsing have significantly better performance across years on the polarity task.

stock price movement
semantic frame parser
news
specific company
predictive model


Specialized Models and Ranking for Coreference Resolution

Abstract: This paper investigates two strategies for improving coreference resolution: (1) training separate models that specialize in particular types of mentions (e.g., pronouns versus proper nouns) and (2) using a ranking loss function rather than a classification function. In addition to being conceptually simple, these modifications of the standard single-model, classification-based approach also deliver significant performance improvements. Specifically, we show that on the ACE corpus both strategies produce f-score gains of more than 3% across the three coreference evaluation metrics (MUC, B3, and CEAF).

coreference resolution
mention
classification function
ranker
candidate antecedent


CODRA: A Novel Discriminative Framework for Rhetorical Analysis

Abstract: Clauses and sentences rarely stand on their own in an actual discourse; rather, the relationship between them carries important information that allows the discourse to express a meaning as a whole beyond the sum of its individual parts. Rhetorical analysis seeks to uncover this coherence structure. In this article, we present CODRA— a COmplete probabilistic Discriminative framework for performing Rhetorical Analysis in accordance with Rhetorical Structure Theory, which posits a tree representation of a discourse.CODRA comprises a discourse segmenter and a discourse parser. First, the discourse segmenter, which is based on a binary classifier, identifies the elementary discourse units in a given text. Then the discourse parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intra-sentential parsing and the other for multi-sentential parsing. We present two approaches to combine these two stages of parsing effectively. By conducting a series of empirical evaluations over two different data sets, we demonstrate that CODRA significantly outperforms the state-of-the-art, often by a wide margin. We also show that a reranking of the k-best parse hypotheses generated by CODRA can potentially improve the accuracy even further.

rhetorical analysis
conditional random fields
multi-sentential parsing
codra
unlabelled structure


Adaptive Language and Translation Models for Interactive Machine Translation

Abstract: We describe experiments carried out with adaptive language and translation models in the context of an interactive computer-assisted translation program. We developed cache-based language models which were then extended to the bilingual case for a cachebased translation model. We present the improvements we obtained in two contexts: in a theoretical setting, we achieved a drop in perplexity for the new models and, in a more practical situation simulating a user working with the system, we showed that fewer keystrokes would be needed to enter a translation.

interactive machine translation
adaptation
model extension
imt system
cache-based translation model


Fast Consensus Decoding over Translation Forests

Abstract: The minimum Bayes risk (MBR) decoding objective improves BLEU scores for machine translation output relative to the standard Viterbi objective of maximizing model score. However, MBR targeting BLEU is prohibitively slow to optimize over k-best lists for large k. In this paper, we introduce and analyze an alternative to MBR that is equally effective at improving performance, yet is asymptotically faster --- running 80 times faster than MBR in experiments with 1000-best lists. Furthermore, our fast decoding procedure can select output sentences based on distributions over entire forests of translations, in addition to k-best lists. We evaluate our procedure on translation forests from two large-scale, state-of-the-art hierarchical machine translation systems. Our forest-based decoding objective consistently outperforms k-best list MBR, giving improvements of up to 1.0 BLEU.


Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation

Abstract: A large-scale parallel corpus is required to train encoder-decoder neural machine translation. The method of using synthetic parallel texts, in which target monolingual corpora are automatically translated into source sentences, is effective in improving the decoder, but is unreliable for enhancing the encoder. In this paper, we propose a method that enhances the encoder and attention using target monolingual corpora by generating multiple source sentences via sampling. By using multiple source sentences, diversity close to that of humans is achieved. Our experimental results show that the translation quality is improved by increasing the number of synthetic source sentences for each given target sentence, and quality close to that using a manually created parallel corpus was achieved.

target monolingual corpora
neural machine translation
back-translation


Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks

Abstract: Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasonable predictions. Herein, we propose Neural-Symbolic Solver (NS-Solver) to explicitly and seamlessly incorporate different levels of symbolic constraints by auxiliary tasks. Our NS-Solver consists of a problem reader to encode problems, a programmer to generate symbolic equations, and a symbolic executor to obtain answers. Along with target expression supervision, our solver is also optimized via 4 new auxiliary objectives to enforce different symbolic reasoning: a) self-supervised number prediction task predicting both number quantity and number locations; b) commonsense constant prediction task predicting what prior knowledge (e.g. how many legs a chicken has) is required; c) program consistency checker computing the semantic loss between predicted equation and target equation to ensure reasonable equation mapping; d) duality exploiting task exploiting the quasi-duality between symbolic equation generation and problem's part-of-speech generation to enhance the understanding ability of a solver. Besides, to provide a more realistic and challenging benchmark for developing a universal and scalable solver, we also construct a new largescale MWP benchmark CM17K consisting of 4 kinds of MWPs (arithmetic, one-unknown linear, one-unknown non-linear, equation set) with more than 17K samples. Extensive experiments on Math23K and our CM17k demonstrate the superiority of our NS-Solver compared to state-of-the-art methods.

math word problem
auxiliary task
neural-symbolic solver


Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers

Abstract: To build an interpretable neural text classifier, most of the prior work has focused on designing inherently interpretable models or finding faithful explanations. A new line of work on improving model interpretability has just started, and many existing methods require either prior information or human annotations as additional inputs in training. To address this limitation, we propose the variational word mask (VMASK) method to automatically learn task-specific important words and reduce irrelevant information on classification, which ultimately improves the interpretability of model predictions. The proposed method is evaluated with three neural text classifiers (CNN, LSTM, and BERT) on seven benchmark text classification datasets. Experiments show the effectiveness of VMASK in improving both model prediction accuracy and interpretability.

variational word mask
interpretability
neural text classifier
information bottleneck


Broad-Coverage Sense Disambiguation and Information Extraction with a Supersense Sequence Tagger

Abstract: In this paper we approach word sense disambiguation and information extraction as a unified tagging problem. The task consists of annotating text with the tagset defined by the 41 Wordnet supersense classes for nouns and verbs. Since the tagset is directly related to Wordnet synsets, the tagger returns partial word sense disambiguation. Furthermore, since the noun tags include the standard named entity detection classes -- person, location, organization, time, etc. -- the tagger, as a by-product, returns extended named entity information. We cast the problem of supersense tagging as a sequential labeling task and investigate it empirically with a discriminatively-trained Hidden Markov Model. Experimental evaluation on the main sense-annotated datasets available, i.e., Semcor and Senseval, shows considerable improvements over the best known "first-sense" baseline.

information extraction
tagset
supersense tagging
sense-grouping


Deep Residual Learning for Weakly-Supervised Relation Extraction

Abstract: Deep residual learning (ResNet) is a new method for training very deep neural networks using identity mapping for shortcut connections. ResNet has won the ImageNet ILSVRC 2015 classification task, and achieved state-of-the-art performances in many computer vision tasks. However, the effect of residual learning on noisy natural language processing tasks is still not well understood. In this paper, we design a novel convolutional neural network (CNN) with residual learning, and investigate its impacts on the task of distantly supervised noisy relation extraction. In contradictory to popular beliefs that ResNet only works well for very deep networks, we found that even with 9 layers of CNNs, using identity mapping could significantly improve the performance for distantly-supervised relation extraction.

relation extraction
identity mapping
cnn
deep residual learning


The Entropy Rate Principle as a Predictor of Processing Effort: An Evaluation against Eye-tracking Data

Abstract: This paper provides evidence for Genzel and Charniak’s (2002) entropy rate principle, which predicts that the entropy of a sentence increases with its position in the text. We show that this principle holds for individual sentences (not just for averages), but we also find that the entropy rate effect is partly an artifact of sentence length, which also correlates with sentence position. Secondly, we evaluate a set of predictions that the entropy rate principle makes for human language processing; using a corpus of eye-tracking data, we show that entropy and processing effort are correlated, and that processing effort is constant throughout a text.

entropy rate principle
processing effort
eye-tracking data
sentence position
signal


Improving Unsupervised Dependency Parsing with Richer Contexts and Smoothing

Abstract: Unsupervised grammar induction models tend to employ relatively simple models of syntax when compared to their supervised counterparts. Traditionally, the unsupervised models have been kept simple due to tractability and data sparsity concerns. In this paper, we introduce basic valence frames and lexical information into an unsupervised dependency grammar inducer and show how this additional information can be leveraged via smoothing. Our model produces state-of-the-art results on the task of unsupervised grammar induction, improving over the best previous work by almost 10 percentage points.

smoothing
grammar induction
dependency model


AMR Parsing as Graph Prediction with Latent Alignment

Abstract: Abstract meaning representations (AMRs) are broad-coverage sentence-level semantic representations. AMRs represent sentences as rooted labeled directed acyclic graphs. AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences. We introduce a neural parser which treats alignments as latent variables within a joint probabilistic model of concepts, relations and alignments. As exact inference requires marginalizing over alignments and is infeasible, we use the variational autoencoding framework and a continuous relaxation of the discrete alignments. We show that joint modeling is preferable to using a pipeline of align and parse. The parser achieves the best reported results on the standard benchmark (74.4% on LDC2016E25).

node
latent variable
probabilistic model
amr parsing
pipeline design


ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language

Abstract: Transformers have been shown to emulate logical deduction over natural language theories (logical rules expressed in natural language), reliably assigning true/false labels to candidate implications. However, their ability to generate implications of a theory has not yet been demonstrated, and methods for reconstructing proofs of answers are imperfect. In this work we show that a generative model, called ProofWriter, can reliably generate both implications of a theory and the natural language proof(s) that support them. In particular, iterating a 1-step implication generator results in proofs that are highly reliable, and represent actual model decisions (rather than post-hoc rationalizations). On the RuleTaker dataset, the accuracy of ProofWriter's proofs exceed previous methods by +9% absolute, and in a way that generalizes to proof depths unseen in training and on out-of-domain problems. We also show that generative techniques can perform a type of abduction with high precision: Given a theory and an unprovable conclusion, identify a missing fact that allows the conclusion to be proved, along with a proof. These results significantly improve the viability of neural methods for systematically reasoning over natural language.

abduction
depth
generative technique
proofwriter
entailment tree


Dimensions of Abusive Language on Twitter

Abstract: In this paper, we use a new categorical form of multidimensional register analysis to identify the main dimensions of functional linguistic variation in a corpus of abusive language, consisting of racist and sexist Tweets. By analysing the use of a wide variety of parts-of-speech and grammatical constructions, as well as various features related to Twitter and computer-mediated communication, we discover three dimensions of linguistic variation in this corpus, which we interpret as being related to the degree of interactive, antagonistic and attitudinal language exhibited by individual Tweets. We then demonstrate that there is a significant functional difference between racist and sexist Tweets, with sexists Tweets tending to be more interactive and attitudinal than racist Tweets.

abusive language
twitter
sexist tweet
multidimensional linguistic variation


SemEval-2013 Task 4: Free Paraphrases of Noun Compounds

Abstract: In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results. The task is to capture some of the meaning of English noun compounds via paraphrasing. Given a two-word noun compound, the participating system is asked to produce an explicitly ranked list of its free-form paraphrases. The list is automatically compared and evaluated against a similarly ranked list of paraphrases proposed by human annotators, recruited and managed through Amazon’s Mechanical Turk. The comparison of raw paraphrases is sensitive to syntactic and morphological variation. The “gold” ranking is based on the relative popularity of paraphrases among annotators. To make the ranking more reliable, highly similar paraphrases are grouped, so as to downplay superficial differences in syntax and morphology. Three systems participated in the task. They all beat a simple baseline on one of the two evaluation measures, but not on both measures. This shows that the task is difficult.

paraphrase
noun compound
annotator
semeval-2013 task


Pun Generation with Surprise

Abstract: We tackle the problem of generating a pun sentence given a pair of homophones (e.g., “died” and “dyed”). Puns are by their very nature statistically anomalous and not amenable to most text generation methods that are supervised by a large corpus. In this paper, we propose an unsupervised approach to pun generation based on lots of raw (unhumorous) text and a surprisal principle. Specifically, we posit that in a pun sentence, there is a strong association between the pun word (e.g., “dyed”) and the distant context, but a strong association between the alternative word (e.g., “died”) and the immediate context. We instantiate the surprisal principle in two ways: (i) as a measure based on the ratio of probabilities given by a language model, and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram model. Based on human evaluation, our retrieve-and-edit approach generates puns successfully 30% of the time, doubling the success rate of a neural generation baseline.

surprisal principle
strong association
immediate context
pun generation


Multi-Domain Neural Machine Translation

Abstract: We present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domainsasdistinctlanguagesandusemultilingual NMT methods to create multi-domain translation systems; we show that this approach results in significant translation quality gains over fine-tuning. We also explore whether the knowledge of pre-specified text domains is necessary; turns out that it is after all, but also that when it is not known quite high translation quality can be reached, and even higher than with known domains in some cases.

neural machine translation
domain token
multi-domain nmt
cluster


Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses

Abstract: Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications. We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency. Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to state-of-the-art GEC systems. We demonstrate that a factor behind this is the inability of systems to rely on a strong internal language model in low error density domains. We hope this work shall facilitate the development of open-domain GEC models that generalize to different topics and genres.

new benchmark
website text
learner essay
language model
grammatical error correction


Adversarial Contrastive Estimation

Abstract: Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data. We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics.

contrastive estimation
sampler
hard negative example


mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

Abstract: The recent “Text-to-Text Transfer Transformer” (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. We also describe a simple technique to prevent “accidental translation” in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model checkpoints used in this work are publicly available.

pre-training
text-to-text transformer
language model
cross-lingual transfer
input text


Assessing the relative reading level of sentence pairs for text simplification

Abstract: While the automatic analysis of the readability of texts has a long history, the use of readability assessment for text simplification has received only little attention so far. In this paper, we explore readability models for identifying differences in the reading levels of simplified and unsimplified versions of sentences. Our experiments show that a relative ranking is preferable to an absolute binary one and that the accuracy of identifying relative simplification depends on the initial reading level of the unsimplified version. The approach is particularly successful in classifying the relative reading level of harder sentences. In terms of practical relevance, the approach promises to be useful for identifying particularly relevant targets for simplification and to evaluate simplifications given specific readability constraints.

relative reading level
text simplification
language learner
different level


Interpretable Neural Predictions with Differentiable Binary Variables

Abstract: The success of neural networks comes hand in hand with a desire for more interpretability. We focus on text classifiers and make them more interpretable by having them provide a justification–a rationale–for their predictions. We approach this problem by jointly training two neural network models: a latent model that selects a rationale (i.e. a short and informative part of the input text), and a classifier that learns from the words in the rationale alone. Previous work proposed to assign binary latent masks to input positions and to promote short selections via sparsity-inducing penalties such as L0 regularisation. We propose a latent model that mixes discrete and continuous behaviour allowing at the same time for binary selections and gradient-based training without REINFORCE. In our formulation, we can tractably compute the expected value of penalties such as L0, which allows us to directly optimise the model towards a pre-specified text selection rate. We show that our approach is competitive with previous work on rationale extraction, and explore further uses in attention mechanisms.

latent model
rationale
mask
text classification
inter-pretable model


Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification

Abstract: Distinguishing between paradigmatic relations such as synonymy, antonymy and hypernymy is an important prerequisite in a range of NLP applications. In this paper, we explore discourse relations as an alternative set of features to lexico-syntactic patterns. We demonstrate that statistics over discourse relations, collected via explicit discourse markers as proxies, can be utilized as salient indicators for paradigmatic relations in multiple languages, outperforming patterns in terms of recall and F1-score. In addition, we observe that markers and patterns provide complementary information, leading to significant classification improvements when applied in combination.

discourse marker
paradigmatic relation
antonymy


MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering, and Speech Commands for Post-Editing Machine Translation

Abstract: The shift from traditional translation to post-editing (PE) of machine-translated (MT) text can save time and reduce errors, but it also affects the design of translation interfaces, as the task changes from mainly generating text to correcting errors within otherwise helpful translation proposals. Since this paradigm shift offers potential for modalities other than mouse and keyboard, we present MMPE, the first prototype to combine traditional input modes with pen, touch, and speech modalities for PE of MT. Users can directly cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place. All text manipulations are logged in an easily interpretable format to simplify subsequent translation process research. The results of an evaluation with professional translators suggest that pen and touch interaction are suitable for deletion and reordering tasks, while speech and multi-modal combinations of select & speech are considered suitable for replacements and insertions. Overall, experiment participants were enthusiastic about the new modalities and saw them as useful extensions to mouse & keyboard, but not as a complete substitute.


A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes

Abstract: We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor processes which produce power-law distributions more closely resembling those in natural languages. We show that an approximation to the hierarchical Pitman-Yor language model recovers the exact formulation of interpolated Kneser-Ney, one of the best smoothing methods for n-gram language models. Experiments verify that our model gives cross entropy results superior to interpolated Kneser-Ney and comparable to modified Kneser-Ney.

bayesian language model
pitman-yor process
formulation
dirichlet process


Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification

Abstract: Neural methods for sentiment analysis have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative differences. Therefore, it is not clear what outstanding conceptual challenges for sentiment analysis remain. In this work, we attempt to discover what challenges still prove a problem for sentiment classifiers for English and to provide a challenging dataset. We collect the subset of sentences that an (oracle) ensemble of state-of-the-art sentiment classifiers misclassify and then annotate them for 18 linguistic and paralinguistic phenomena, such as negation, sarcasm, modality, etc. Finally, we provide a case study that demonstrates the usefulness of the dataset to probe the performance of a given sentiment classifier with respect to linguistic phenomena.

ensemble
negation
sarcasm
sentiment analysis


A Discriminative Model for Tree-to-Tree Translation

Abstract: This paper proposes a statistical, tree-to-tree model for producing translations. Two main contributions are as follows: (1) a method for the extraction of syntactic structures with alignment information from a parallel corpus of translations, and (2) use of a discriminative, feature-based model for prediction of these target-language syntactic structures---which we call aligned extended projections, or AEPs. An evaluation of the method on translation from German to English shows similar performance to the phrase-based model of Koehn et al. (2003).


Computing Lexical Contrast

Abstract: Knowing the degree of semantic contrast between words has widespread application in natural language processing, including machine translation, information retrieval, and dialogue systems. Manually created lexicons focus on opposites, such as hot and cold. Opposites are of many kinds such as antipodals, complementaries, and gradable. Existing lexicons often do not classify opposites into the different kinds, however. They also do not explicitly list word pairs that are not opposites but yet have some degree of contrast in meaning, such as warm and cold or tropical and freezing. We propose an automatic method to identify contrasting word pairs that is based on the hypothesis that if a pair of words, A and B, are contrasting, then there is a pair of opposites, C and D, such that A and C are strongly related and B and D are strongly related. (For example, there exists the pair of opposites hot and cold such that tropical is related to hot, and freezing is related to cold.) We will call this the contrast hypothesis.We begin with a large crowdsourcing experiment to determine the amount of human agreement on the concept of oppositeness and its different kinds. In the process, we flesh out key features of different kinds of opposites. We then present an automatic and empirical measure of lexical contrast that relies on the contrast hypothesis, corpus statistics, and the structure of a Roget-like thesaurus. We show how, using four different data sets, we evaluated our approach on two different tasks, solving “most contrasting word” questions and distinguishing synonyms from opposites. The results are analyzed across four parts of speech and across five different kinds of opposites. We show that the proposed measure of lexical contrast obtains high precision and large coverage, outperforming existing methods.

opposite
word pair
automatic method
ranking
negation


Semantic Roles for SMT: A Hybrid Two-Pass Model

Abstract: We present results on a novel hybrid semantic SMT model that incorporates the strengths of both semantic role labeling and phrase-based statistical machine translation. The approach avoids major complexity limitations via a two-pass architecture. The first pass is performed using a conventional phrase-based SMT model. The second pass is performed by a re-ordering strategy guided by shallow semantic parsers that produce both semantic frame and role labels. Evaluation on a Wall Street Journal newswire genre test set showed the hybrid model to yield an improvement of roughly half a point in BLEU score over a strong pure phrase-based SMT baseline -- to our knowledge, the first successful application of semantic role labeling to SMT.

smt model
semantic role
cross-lingual match


Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

Abstract: Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia). In this paper, we propose a novel embedding method specifically designed for NED. The proposed method jointly maps words and entities into the same continuous vector space. We extend the skip-gram model by using two models. The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words. By combining contexts based on the proposed embedding with standard NED features, we achieved state-of-the-art accuracy of 93.1% on the standard CoNLL dataset and 85.2% on the TAC 2010 dataset.

named entity disambiguation
skip-gram model
knowledge graph


AMR dependency parsing with a typed semantic algebra

Abstract: We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.

algebra
compositional structure
amr
dependency parser


Using Semantic Roles to Improve Question Answering

Abstract: Shallow semantic parsing, the automatic identification and labeling of sentential constituents, has recently received much attention. Our work examines whether semantic role information is beneficial to question answering. We introduce a general framework for answer extraction which exploits semantic role annotations in the FrameNet paradigm. We view semantic role assignment as an optimization problem in a bipartite graph and answer extraction as an instance of graph matching. Experimental results on the TREC datasets demonstrate improvements over state-of-the-art models.

question answering
role labeling
predicate argument structure


Effective Use of Linguistic and Contextual Information for Statistical Machine Translation

Abstract: Current methods of using lexical features in machine translation have difficulty in scaling up to realistic MT tasks due to a prohibitively large number of parameters involved. In this paper, we propose methods of using new linguistic and contextual features that do not suffer from this problem and apply them in a state-of-the-art hierarchical MT system. The features used in this work are non-terminal labels, non-terminal length distribution, source string context and source dependency LM scores. The effectiveness of our techniques is demonstrated by significant improvements over a strong base-line. On Arabic-to-English translation, improvements in lower-cased BLEU are 2.0 on NIST MT06 and 1.7 on MT08 newswire data on decoding output. On Chinese-to-English translation, the improvements are 1.0 on MT06 and 0.8 on MT08 newswire data.

contextual information
statistical machine translation
string-to-dependency language model
constraint modeling


Report of NEWS 2010 Transliteration Generation Shared Task

Abstract: This report documents the Transliteration Generation Shared Task conducted as a part of the Named Entities Workshop (NEWS 2010), an ACL 2010 workshop. The shared task features machine transliteration of proper names from English to 9 languages and from 3 languages to English. In total, 12 tasks are provided. 7 teams from 5 different countries participated in the evaluations. Finally, 33 standard and 8 non-standard runs are submitted, where diverse transliteration methodologies are explored and reported on the evaluation data. We report the results with 4 performance metrics. We believe that the shared task has successfully achieved its objective by providing a common benchmarking platform for the research community to evaluate the state-of-the-art technologies that benefit the future research and development.

transliteration
named entities workshop
news
direction
quality metric


Recursive Deep Models for Discourse Parsing

Abstract: Text-level discourse parsing remains a challenge: most approaches employ features that fail to capture the intentional, semantic, and syntactic aspects that govern discourse coherence. In this paper, we propose a recursive model for discourse parsing that jointly models distributed representations for clauses, sentences, and entire discourses. The learned representations can to some extent learn the semantic and intentional import of words and larger discourse units automatically,. The proposed framework obtains comparable performance regarding standard discoursing parsing evaluations when compared against current state-of-art systems.

discourse
recursive neural network
edu
network model


Identifying hypernyms in distributional semantic spaces

Abstract: In this paper we apply existing directional similarity measures to identify hypernyms with a state-of-the-art distributional semantic model. We also propose a new directional measure that achieves the best performance in hypernym identification.

hypernym
semantic relation
distributional inclusion hypothesis


Learning Latent Personas of Film Characters

Abstract: We present two latent variable models for learning character types, or personas, in film, in which a persona is defined as a set of mixtures over latent lexical classes. These lexical classes capture the stereotypical actions of which a character is the agent and patient, as well as attributes by which they are described. As the first attempt to solve this problem explicitly, we also present a new dataset for the text-driven analysis of film, along with a benchmark testbed to help drive future work in this area.

persona
character
new dataset
text-driven analysis
movie plot summary


Locally Training the Log-Linear Model for SMT

Abstract: In statistical machine translation, minimum error rate training (MERT) is a standard method for tuning a single weight with regard to a given development data. However, due to the diversity and uneven distribution of source sentences, there are two problems suffered by this method. First, its performance is highly dependent on the choice of a development set, which may lead to an unstable performance for testing. Second, translations become inconsistent at the sentence level since tuning is performed globally on a document level. In this paper, we propose a novel local training method to address these two problems. Unlike a global training method, such as MERT, in which a single weight is learned and used for all the input sentences, we perform training and testing in one step by learning a sentence-wise weight for each input sentence. We propose efficient incremental training methods to put the local training into practice. In NIST Chinese-to-English translation tasks, our local training method significantly outperforms MERT with the maximal improvements up to 2.0 BLEU points, meanwhile its efficiency is comparable to that of the global method.

log-linear model
machine translation
weight
local training method


Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering

Abstract: We posed the shared task of assigning sentence-level quality scores for a very noisy corpus of sentence pairs crawled from the web, with the goal of sub-selecting 1% and 10% of high-quality data to be used to train machine translation systems. Seventeen participants from companies, national research labs, and universities participated in this task.

shared task
parallel corpus filtering
sentence pair
wmt18


Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach

Abstract: In this paper, we investigate a challenging task of automatic related work generation. Given multiple reference papers as input, the task aims to generate a related work section for a target paper. The generated related work section can be used as a draft for the author to complete his or her final related work section. We propose our Automatic Related Work Generation system called ARWG to address this task. It first exploits a PLSA model to split the sentence set of the given papers into different topic-biased parts, and then applies regression models to learn the importance of the sentences. At last it employs an optimization framework to generate the related work section. Our evaluation results on a test set of 150 target papers along with their reference papers show that our proposed ARWG system can generate related work sections with better quality. A user study is also performed to show ARWG can achieve an improvement over generic multi-document summarization baselines.

work section
work generation
topic-biased part


Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity

Abstract: Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies. We introduce a simple paraphrase generation algorithm which discourages the production of n-grams that are present in the input. Our approach enables paraphrase generation in many languages from a single multilingual NMT model. Furthermore, the amount of lexical diversity between the input and output can be controlled at generation time. We conduct a human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our method produces paraphrases that better preserve meaning and are more gramatical, for the same level of lexical diversity. Additional smaller human assessments demonstrate our approach also works in two non-English languages.

multilingual translation
n-gram
paraphrase generation


Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task

Abstract: Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10% M on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2% on the CoNLL-2014 benchmark and by 4% on JFLEG.

error correction
machine translation
neural gec system
ungrammatical text


Bootstrapping Deep Lexical Resources: Resources for Courses

Abstract: We propose a range of deep lexical acquisition methods which make use of morphological, syntactic and ontological language resources to model word similarity and bootstrap from a seed lexicon. The different methods are deployed in learning lexical items for a precision grammar, and shown to each have strengths and weaknesses over different word classes. A particular focus of this paper is the relative accessibility of different language resource types, and predicted "bang for the buck" associated with each in deep lexical acquisition applications.

lexical acquisition
item
dla
deep grammar
secondary language resource


The Multilingual Paraphrase Database

Abstract: We release a massive expansion of the paraphrase database (PPDB) that now includes a collection of paraphrases in 23 different languages. The resource is derived from large volumes of bilingual parallel data. Our collection is extracted and ranked using state of the art methods. The multilingual PPDB has over a billion paraphrase pairs in total, covering the following languages: Arabic, Bulgarian, Chinese, Czech, Dutch, Estonian, Finnish, French, German, Greek, Hungarian, Italian, Latvian, Lithuanian, Polish, Portugese, Romanian, Russian, Slovak, Slovenian, and Swedish.

multilingual paraphrase database
ppdb
parallel text


Annotation of Quantification: The Current State of ISO 24617-12

Abstract: This paper discusses the current state of developing an ISO standard annotation scheme for quantification phenomena in natural language, as part of the ISO Semantic Annotation Framework (ISO 24617). A proposed approach that combines ideas from the theory of generalised quantifiers and from neo-Davidsonian event semantics was adopted by the ISO organisation in 2019 as a starting point for developing such an annotation scheme. * This scheme consists of (1) a conceptual `metamodel' that visualises the types of entities, functions and relations that go into annotations of quantification; (2) an abstract syntax which defines `annotation structures' as triples and other set-theoretic constructs; (3) an XML-based representation of annotation structures (`concrete syntax'); and (4) a compositional semantics of annotation structures. The latter three components together define the interpreted markup language QuantML. The focus in this paper is on the structuring of the semantic information needed to characterise quantification in natural language and the representation of these structures in QuantML.

current state
iso
annotation scheme
quantification phenomenon


Bridging SMT and TM with Translation Recommendation

Abstract: We propose a translation recommendation framework to integrate Statistical Machine Translation (SMT) output with Translation Memory (TM) systems. The framework recommends SMT outputs to a TM user when it predicts that SMT outputs are more suitable for post-editing than the hits provided by the TM. We describe an implementation of this framework using an SVM binary classifier. We exploit methods to fine-tune the classifier and investigate a variety of features of different types. We rely on automatic MT evaluation metrics to approximate human judgements in our experiments. Experimental results show that our system can achieve 0.85 precision at 0.89 recall, excluding exact matches. Furthermore, it is possible for the end-user to achieve a desired balance between precision and recall by adjusting confidence levels.

translation memory
smt output
translation recommendation system
post-editor
sentence level


Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data

Abstract: Argument compatibility is a linguistic condition that is frequently incorporated into modern event coreference resolution systems. If two event mentions have incompatible arguments in any of the argument roles, they cannot be coreferent. On the other hand, if these mentions have compatible arguments, then this may be used as information towards deciding their coreferent status. One of the key challenges in leveraging argument compatibility lies in the paucity of labeled data. In this work, we propose a transfer learning framework for event coreference resolution that utilizes a large amount of unlabeled data to learn argument compatibility of event mentions. In addition, we adopt an interactive inference network based model to better capture the compatible and incompatible relations between the context words of event mentions. Our experiments on the KBP 2017 English dataset confirm the effectiveness of our model in learning argument compatibility, which in turn improves the performance of the overall event coreference model.

event coreference resolution
unlabeled data
mention


Crystal: Analyzing Predictive Opinions on the Web

Abstract: In this paper, we present an election prediction system (Crystal) based on web users’ opinions posted on an election prediction website. Given a prediction message, Crystal first identifies which party the message predicts to win and then aggregates prediction analysis results of a large amount of opinions to project the election results. We collect past election prediction messages from the Web and automatically build a gold standard. We focus on capturing lexical patterns that people frequently use when they express their predictive opinions about a coming election. To predict election results, we apply SVM-based supervised learning. To improve performance, we propose a novel technique which generalizes n-gram feature patterns. Experimental results show that Crystal significantly outperforms several baselines as well as a non-generalized n-gram approach. Crystal predicts future elections with 81.68% accuracy.


An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models

Abstract: Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examples are scarce, pre-trained models perform as poorly as models trained from scratch. In the case of extreme minority, we propose to use multi-task learning (MTL) to improve generalization. Our experiments on natural language inference and paraphrase identification show that MTL with the right auxiliary tasks significantly improves performance on challenging examples without hurting the in-distribution performance. Further, we show that the gain from MTL mainly comes from improved generalization from the minority examples. Our results highlight the importance of data diversity for overcoming spurious correlations.1

robustness
language model
pre-trained model
auxiliary task
plm


Forest Rescoring: Faster Decoding with Integrated Language Models

Abstract: Efficient decoding has been a fundamental problem in machine translation, especially with an integrated language model which is essential for achieving good translation quality. We develop faster approaches for this problem based on k-best parsing algorithms and demonstrate their effectiveness on both phrase-based and syntax-based MT systems. In both cases, our methods achieve significant speed improvements, often by more than a factor of ten, over the conventional beam-search method at the same levels of search error and translation accuracy.

machine translation
forest rescoring
hiero search refinements


A Bayesian Approach to Unsupervised Semantic Role Induction

Abstract: We introduce two Bayesian models for unsupervised semantic role labeling (SRL) task. The models treat SRL as clustering of syntactic signatures of arguments with clusters corresponding to semantic roles. The first model induces these clusterings independently for each predicate, exploiting the Chinese Restaurant Process (CRP) as a prior. In a more refined hierarchical model, we inject the intuition that the clusterings are similar across different predicates, even though they are not necessarily identical. This intuition is encoded as a distance-dependent CRP with a distance between two syntactic signatures indicating how likely they are to correspond to a single semantic role. These distances are automatically induced within the model and shared across predicates. Both models achieve state-of-the-art results when evaluated on PropBank, with the coupled model consistently outperforming the factored counterpart in all experimental set-ups.

semantic role
bayesian model
argument signature


Phrase-Based Backoff Models for Machine Translation of Highly Inflected Languages

Abstract: We propose a backoff model for phrasebased machine translation that translates unseen word forms in foreign-language text by hierarchical morphological abstractions at the word and the phrase level. The model is evaluated on the Europarl corpus for German-English and FinnishEnglish translation and shows improvements over state-of-the-art phrase-based models.

backoff model
machine translation
french


Measuring Distributional Similarity in Context

Abstract: The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and paraphrases to word sense disambiguation and textual entailment. Vector-based models are typically directed at representing words in isolation and thus best suited for measuring similarity out of context. In his paper we propose a probabilistic framework for measuring similarity in context. Central to our approach is the intuition that word meaning is represented as a probability distribution over a set of latent senses and is modulated by context. Experimental results on lexical substitution and word similarity show that our algorithm outperforms previously proposed models.

sense disambiguation
probabilistic framework
substitution
latent variable


LORELEI Language Packs: Data, Tools, and Resources for Technology Development in Low Resource Languages

Abstract: In this paper, we describe the textual linguistic resources in nearly 3 dozen languages being produced by Linguistic Data Consortium for DARPA's LORELEI (Low Resource Languages for Emergent Incidents) Program. The goal of LORELEI is to improve the performance of human language technologies for low-resource languages and enable rapid re-training of such technologies for new languages, with a focus on the use case of deployment of resources in sudden emergencies such as natural disasters. Representative languages have been selected to provide broad typological coverage for training, and surprise incident languages for testing will be selected over the course of the program. Our approach treats the full set of language packs as a coherent whole, maintaining LORELEI-wide specifications, tagsets, and guidelines, while allowing for adaptation to the specific needs created by each language. Each representative language corpus, therefore, both stands on its own as a resource for the specific language and forms part of a large multilingual resource for broader cross-language technology development.

technology development
low resource languages
lorelei


Tkol, Httt, and r/radiohead: High Affinity Terms in Reddit Communities

Abstract: Language is an important marker of a cultural group, large or small. One aspect of language variation between communities is the employment of highly specialized terms with unique significance to the group. We study these high affinity terms across a wide variety of communities by leveraging the rich diversity of Reddit.com. We provide a systematic exploration of high affinity terms, the often rapid semantic shifts they undergo, and their relationship to subreddit characteristics across 2600 diverse subreddits. Our results show that high affinity terms are effective signals of loyal communities, they undergo more semantic shift than low affinity terms, and that they are partial barrier to entry for new users. We conclude that Reddit is a robust and valuable data source for testing further theories about high affinity terms across communities.


Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling

Abstract: Semantic role labeling (SRL) is crucial to natural language understanding as it identifies the predicate-argument structure in text with semantic labels. Unfortunately, resources required to construct SRL models are expensive to obtain and simply do not exist for most languages. In this paper, we present a two-stage method to enable the construction of SRL models for resourcepoor languages by exploiting monolingual SRL and multilingual parallel data. Experimental results show that our method outperforms existing methods. We use our method to generate Proposition Banks with high to reasonable quality for 7 languages in three language families and release these resources to the research community.

annotation projection
propbanks
new target language


Searching for Effective Neural Extractive Summarization: What Works and What's Next

Abstract: The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or how they might be improved. In this paper, we seek to better understand how neural extractive summarization systems could benefit from different types of model architectures, transferable knowledge and learning schemas. Besides, we find an effective way to improve the current framework and achieve the state-of-the-art result on CNN/DailyMail by a large margin based on our observations and analysis. Hopefully, our work could provide more hints for future research on extractive summarization.

summarization
deep neural network
sentence embedding


String-to-Dependency Statistical Machine Translation

Abstract: We propose a novel string-to-dependency algorithm for statistical machine translation. This algorithm employs a target dependency language model during decoding to exploit long distance word relations, which cannot be modeled with a traditional n-gram language model. Experiments show that the algorithm achieves significant improvement in MT performance over a state-of-the-art hierarchical string-to-string system on NIST MT06 and MT08 newswire evaluation sets.

machine translation
string-to-dependency model
neighbouring word
target side


Discriminant-Based MRS Banking

Abstract: We present an approach to discriminant-based MRS banking, i.e. the construction of an annotated corpus where each input item is paired with a logical-form semantics. Semantic annotations are produced by parsing with a broad-coverage precision grammar, followed by manual disambiguation. The selection of the preferred analysis for each item (and hence its semantic form) builds on a notion of semantic discriminants, essentially localized dependencies extracted from a full-fledged, underspecified semantic representation.

discriminant-based mrs banking
eds
elementary dependency structures
flavor


Glue Rules for Robust Chart Realization

Abstract: This paper shows how glue rules can be used to increase the robustness of statistical chart realization in a manner inspired by dependency realization. Unlike the use of glue rules in MT---but like previous work with XLE on improving robustness with hand-crafted grammars---they are invoked here as a fall-back option when no grammatically complete realization can be found. The method works with Combinatory Categorial Grammar (CCG) and has been implemented in OpenCCG. As the techniques are not overly tied to CCG, they are expected to be applicable to other grammar-based chart realizers where robustness is a common problem. Unlike an earlier robustness technique of greedily assembling fragments, glue rules enable n-best outputs and are compatible with disjunctive inputs. Experimental results indicate that glue rules yield improved realizations in comparison to greedy fragment assembly, though a sizeable gap remains between the quality of grammatically complete realizations and fragmentary ones.


MasakhaNER: Named Entity Recognition for African Languages

Abstract: We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1

entity recognition
african language
masakhaner


Grouping Multi-word Expressions According to Part-Of-Speech in Statistical Machine Translation

Abstract: This paper studies a strategy for identifying and using multi-word expressions in Statistical Machine Translation. The performance of the proposed strategy for various types of multi-word expressions (like nouns or verbs) is evaluated in terms of alignment quality as well as translation accuracy. Evaluations are performed by using real-life data, namely the European Parliament corpus. Results from translation tasks from English-to-Spanish and from Spanish-to-English are presented and discussed.


Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification

Abstract: This paper studies the task of comparative preference classification (CPC). Given two entities in a sentence, our goal is to classify whether the first (or the second) entity is preferred over the other or no comparison is expressed at all between the two entities. Existing works either do not learn entity-aware representations well and fail to deal with sentences involving multiple entity pairs or use sequential modeling approaches that are unable to capture long-range dependencies between the entities. Some also use traditional machine learning approaches that do not generalize well. This paper proposes a novel Entity-aware Dependency-based Deep Graph Attention Network (ED-GAT) that employs a multi-hop graph attention over a dependency graph sentence representation to leverage both the semantic information from word embeddings and the syntactic information from the dependency graph to solve the problem. Empirical evaluation shows that the proposed model achieves the state-of-the-art performance in comparative preference classification.

graph attention network
comparative preference classification
dependency graph


From Characters to Words to in Between: Do We Capture Morphology?

Abstract: Words can be represented by composing the representations of subword units such as word segments, characters, and/or character n-grams. While such representations are effective and may capture the morphological regularities of words, they have not been systematically compared, and it is not understood how they interact with different morphological typologies. On a language modeling task, we present experiments that systematically vary (1) the basic unit of representation, (2) the composition of these representations, and (3) the morphological typology of the language modeled. Our results extend previous findings that character representations are effective across typologies, and we find that a previously unstudied combination of character trigram representations composed with bi-LSTMs outperforms most others. But we also find room for improvement: none of the character-level models match the predictive accuracy of a model with access to true morphological analyses, even when learned from an order of magnitude more data.

character
segmentation
character-aware nlms
bpe
bilstm


A Unified Tagging Approach to Text Normalization

Abstract: This paper addresses the issue of text normalization, an important yet often overlooked problem in natural language processing. By text normalization, we mean converting ‘informally inputted’ text into the canonical form, by eliminating ‘noises’ in the text and detecting paragraph and sentence boundaries in the text. Previously, text normalization issues were often undertaken in an ad-hoc fashion or studied separately. This paper first gives a formalization of the entire problem. It then proposes a unified tagging approach to perform the task using Conditional Random Fields (CRF). The paper shows that with the introduction of a small set of tags, most of the text normalization tasks can be performed within the approach. The accuracy of the proposed method is high, because the subtasks of normalization are interdependent and should be performed together. Experimental results on email data cleaning show that the proposed method significantly outperforms the approach of using cascaded models and that of employing independent models.

text normalization
noise
conditional random fields
crf


SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)

Abstract: We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.

offensive language identification
social media
semeval-2020 task
multilingual dataset


Uncertain Natural Language Inference

Abstract: We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. We demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a probabilistic scale, where items even with the same categorical label differ in how likely people judge them to be true given a premise. We describe a direct scalar regression modeling approach, and find that existing categorically-labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the categorical bin assignment employed in current NLI tasks.

natural language inference
nli
judgement


COMETA: A Corpus for Medical Entity Linking in the Social Media

Abstract: Whilst there has been growing progress in Entity Linking (EL) for general language, existing datasets fail to address the complex nature of health terminology in layman's language. Meanwhile, there is a growing need for applications that can understand the public's voice in the health domain. To address this we introduce a new corpus called COMETA, consisting of 20k English biomedical entity mentions from Reddit expert-annotated with links to SNOMED CT, a widely-used medical knowledge graph. Our corpus satisfies a combination of desirable properties, from scale and coverage to diversity and quality, that to the best of our knowledge has not been met by any of the existing resources in the field. Through benchmark experiments on 20 EL baselines from string- to neural-based models we shed light on the ability of these systems to perform complex inference on entities and concepts under 2 challenging evaluation scenarios. Our experimental results on COMETA illustrate that no golden bullet exists and even the best mainstream techniques still have a significant performance gap to fill, while the best solution relies on combining different views of data.

entity linking
health terminology
cometa


A Comparison of Features for Automatic Readability Assessment

Abstract: Several sets of explanatory variables - including shallow, language modeling, POS, syntactic, and discourse features - are compared and evaluated in terms of their impact on predicting the grade level of reading material for primary school students. We find that features based on in-domain language models have the highest predictive power. Entity-density (a discourse feature) and POS-features, in particular nouns, are individually very useful but highly correlated. Average sentence length (a shallow feature) is more useful - and less expensive to compute - than individual syntactic features. A judicious combination of features examined here results in a significant improvement over the state of the art.

automatic readability assessment
grade level
lexical chain
reader
discourse-based feature


Morphological Paradigms: Computational Structure and Unsupervised Learning

Abstract: This thesis explores the computational structure of morphological paradigms from the perspective of unsupervised learning. Three topics are studied: (i) stem identification, (ii) paradigmatic similarity, and (iii) paradigm induction. All the three topics progress in terms of the scope of data in question. The first and second topics explore structure when morphological paradigms are given, first within a paradigm and then across paradigms. The third topic asks where morphological paradigms come from in the first place, and explores strategies of paradigm induction from child-directed speech. This research is of interest to linguists and natural language processing researchers, for both theoretical questions and applied areas.

computational structure
unsupervised learning
morphological paradigms


Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances

Abstract: This paper describes DUALIST, an active learning annotation paradigm which solicits and learns from labels on both features (e.g., words) and instances (e.g., documents). We present a novel semi-supervised training algorithm developed for this setting, which is (1) fast enough to support real-time interactive speeds, and (2) at least as accurate as preexisting methods for learning with mixed feature and instance labels. Human annotators in user studies were able to produce near-state-of-the-art classifiers---on several corpora in a variety of application domains---with only a few minutes of effort.

annotator
active learning
few minute
text classification