Updated the dataset card.

README.md

@@ -31,15 +31,14 @@ task_ids:
 # Dataset Card for Wino-X
 
 ## Table of Contents
-- [Table of Contents](#table-of-contents)
 - [Dataset Description](#dataset-description)
   - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
   - [Languages](#languages)
 - [Dataset Structure](#dataset-structure)
   - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
 - [Dataset Creation](#dataset-creation)
   - [Curation Rationale](#curation-rationale)
   - [Source Data](#source-data)
@@ -53,100 +52,164 @@ task_ids:
   - [Dataset Curators](#dataset-curators)
   - [Licensing Information](#licensing-information)
   - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
 
 ## Dataset Description
 
-- **Homepage:**
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:**
+- **Homepage:** [Wino-X repository](https://github.com/demelin/Wino-X)
+- **Repository:** [Wino-X repository](https://github.com/demelin/Wino-X)
+- **Paper:** [Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution](https://aclanthology.org/2021.emnlp-main.670/)
+- **Leaderboard:** [N/A]
+- **Point of Contact:** [Denis Emelin](demelin.github.io)
 
 ### Dataset Summary
 
-[More Information Needed]
+Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English
+counterparts, used to examine whether neural machine translation models can perform coreference resolution that
+requires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across
+multiple languages.
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+- translation: The dataset can be used to evaluate translations of ambiguous source sentences, as produced by translation models . A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
+- coreference-resolution: The dataset can be used to rank alternative translations of an ambiguous source sentence that differ in the chosen referent of an ambiguous source pronoun. A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
+- commonsense-reasoning: The dataset can also be used evaluate whether pretrained multilingual language models can perform commonsense reasoning in (or across) multiple languages by identifying the correct filler in a cloze completion task. An [XLM-based model](https://huggingface.co/xlm-roberta-base) can be used for this purpose.
 
 ### Languages
 
-[More Information Needed]
+The dataset (both its MT and LM portions) is available in the following translation pairs: English-German, English-French, English-Russian. All English sentences included in *Wino-X* were extracted from publicly available parallel corpora, as detailed in the accompanying paper, and represent the dataset-specific language varieties. All non-English sentences were obtained through machine translation and may, as such, exhibit features of translationese.
 
 ## Dataset Structure
 
 ### Data Instances
 
-[More Information Needed]
+The following represents a typical *MT-Wino-X* instance (for the English-German translation pair):  
+  
+{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",  
+"sentence": "The woman looked for a different vase for the bouquet because it was too small.",  
+"translation1": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.",  
+"translation2": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.",  
+"answer": 1,  
+"pronoun1": "sie",  
+"pronoun2": "er",  
+"referent1_en": "vase",  
+"referent2_en": "bouquet",  
+"true_translation_referent_of_pronoun1_de": "Vase",  
+"true_translation_referent_of_pronoun2_de": "Blumenstrauß",  
+"false_translation_referent_of_pronoun1_de": "Vase",  
+ "false_translation_referent_of_pronoun2_de": "Blumenstrauß"}  
+  
+  
+The following represents a typical *LM-Wino-X* instance (for the English-French translation pair):  
+  
+{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",   
+"sentence": "The woman looked for a different vase for the bouquet because it was too small.",   
+"context_en": "The woman looked for a different vase for the bouquet because _ was too small.",   
+"context_fr": "La femme a cherché un vase différent pour le bouquet car _ était trop petit.",   
+"option1_en": "the bouquet",   
+"option2_en": "the vase",   
+"option1_fr": "le bouquet",   
+"option2_fr": "le vase",   
+"answer": 2,   
+"context_referent_of_option1_fr": "bouquet",  
+"context_referent_of_option2_fr": "vase"}
 
 ### Data Fields
 
-[More Information Needed]
+For *MT-Wino-X*:  
+  
+- "qID": Unique identifier ID for this dataset instance.
+- "sentence":  English sentence containing the ambiguous pronoun 'it'.
+- "translation1": First translation candidate.
+- "translation2": Second translation candidate.
+- "answer":  ID of the correct translation.
+- "pronoun1": Translation of the ambiguous source pronoun in translation1.
+- "pronoun2": Translation of the ambiguous source pronoun in translation2.
+- "referent1_en": English referent of the translation of the ambiguous source pronoun in translation1.
+- "referent2_en": English referent of the translation of the ambiguous source pronoun in translation2.
+- "true_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the correct translation.
+- "true_translation_referent_of_pronoun2_[TGT-LANG]":  Target language referent of pronoun2 in the correct translation.
+- "false_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the incorrect translation.
+- "false_translation_referent_of_pronoun2_[TGT-LANG]":  Target language referent of pronoun2 in the incorrect translation.
+  
+  
+For *LM-Wino-X*:  
+  
+- "qID": Unique identifier ID for this dataset instance.
+- "sentence": English sentence containing the ambiguous pronoun 'it'. 
+- "context_en": Same English sentence, where 'it' is replaced by a gap. 
+- "context_fr": Target language translation of the English sentence, where the translation of 'it' is replaced by a gap.
+- "option1_en": First filler option for the English sentence.
+- "option2_en":  Second filler option for the English sentence.
+- "option1_[TGT-LANG]": First filler option for the target language sentence.
+- "option2_[TGT-LANG]":  Second filler option for the target language sentence.
+- "answer": ID of the correct gap filler.
+- "context_referent_of_option1_[TGT-LANG]": English translation of option1_[TGT-LANG].
+- "context_referent_of_option2_[TGT-LANG]": English translation of option2_[TGT-LANG]
 
 ### Data Splits
 
-[More Information Needed]
+*Wno-X* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .
+
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-[More Information Needed]
+Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
 
 ### Source Data
 
 #### Initial Data Collection and Normalization
 
-[More Information Needed]
+Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
 
 #### Who are the source language producers?
 
-[More Information Needed]
+Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
 
 ### Annotations
 
 #### Annotation process
 
-[More Information Needed]
+Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
 
 #### Who are the annotators?
 
-[More Information Needed]
+Annotations were generated automatically and verified by the dataset author / curator for correctness.
 
 ### Personal and Sensitive Information
 
-[More Information Needed]
+[N/A]
 
 ## Considerations for Using the Data
 
 ### Social Impact of Dataset
 
-[More Information Needed]
+Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
 
 ### Discussion of Biases
 
-[More Information Needed]
+Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
 
 ### Other Known Limitations
 
-[More Information Needed]
+Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
 
 ## Additional Information
 
 ### Dataset Curators
 
-[More Information Needed]
+[Denis Emelin](demelin.github.io)
 
 ### Licensing Information
 
-[More Information Needed]
+MIT
 
 ### Citation Information
 
-[More Information Needed]
-
-### Contributions
-
-See the [associated GitHub repository](https://github.com/demelin/Wino-X).
+@inproceedings{Emelin2021WinoXMW,
+  title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},
+  author={Denis Emelin and Rico Sennrich},
+  booktitle={EMNLP},
+  year={2021}
+}

wino_x.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 """ Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English
 counterparts, used to examine whether neural machine translation models can perform coreference resolution that
-requires commonsense knowledge and whether multilingual language models are capable of commonsense reasoning across
+requires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across
 multiple languages. """
 
 import csv