| --- |
| license: mit |
| size_categories: |
| - n<1K |
| task_categories: |
| - text-classification |
| pretty_name: liaisons IBM's Claim Stance Dataset Sample |
| dataset_info: |
| features: |
| - name: topic |
| dtype: string |
| - name: parent_argument |
| dtype: string |
| - name: child_argument |
| dtype: string |
| - name: relation |
| dtype: string |
| splits: |
| - name: binary |
| num_bytes: 21165 |
| num_examples: 110 |
| - name: ternary |
| num_bytes: 13861 |
| num_examples: 72 |
| download_size: 22681 |
| dataset_size: 35026 |
| configs: |
| - config_name: default |
| data_files: |
| - split: binary |
| path: data/binary-* |
| - split: ternary |
| path: data/ternary-* |
| tags: |
| - relation-based argument mining |
| - argument mining |
| - sample |
| --- |
| --- |
| ⚠️ This repository is a part of an academical project for the Heriot-Watt University, no third-party contributions are accepted. |
|
|
| # Dataset Card for Liaison's IBM Claim Stance Dataset Sample |
|
|
| ## Table of Contents |
|
|
| - [About the Dataset](#about-the-dataset) |
| - [About Contributions](#about-contributions) |
| - [Associated Works](#associated-works) |
| - [Licensing Information](#licensing-information) |
| - [Credits](#credits) |
| - [Special Thanks](#special-thanks) |
|
|
| ## About the Dataset |
|
|
| ### Dataset Summary |
|
|
| The present dataset is a result of processing the [IBM Debater Claim Stance Dataset](https://huggingface.co/datasets/ibm/claim_stance) to create representative samples. The size has been reduced to roughly 100 entries, enabling the benchmarking of models for relation-based argument mining tasks with limited resources. |
| You can also find here the associated benchmarking [framework](https://github.com/coding-kelps/liaisons-experiments) and [results](https://huggingface.co/datasets/coding-kelps/liaisons-experiments-results). |
|
|
| The sample also modifies the original dataset to achieve a more balanced plurality of stances and topics, and creates a new "unrelated" class in argument relation (following a simple rule-based data augmentation algorithm). Further details on the preprocessing can be found on [GitHub](https://github.com/coding-kelps/liaisons-preprocess). |
|
|
| ### Dataset Structure |
|
|
| * parent_argument - The first argument that states a position regarding a topic |
| * child_argument - Another argument that is compared to the parent argument |
| * relation - The argumentative relation of the child argument to the parent argument. It can either be support/attack in the binary split or support/attack/unrelated in the ternary split |
|
|
| ## About Contributions |
|
|
| As mentioned earlier, this work is part of an academic project for the validation of my Master's Degree at Heriot-Watt University, preventing me from accepting any contributions until the final release of my project. Thank you for your understanding. |
|
|
| ## Associated Works |
|
|
| This work is part of a collection of works whose ultimate goal is to deliver a framework to automatically analyze social media content (e.g., X, Reddit) to extract their argumentative value and predict their relations, leveraging Large Language Models' (LLMs) abilities: |
|
|
| - [liaisons](https://github.com/coding-kelps/liaisons) (the developed client for social media content analysis) |
| - [liaisons-preprocess](https://github.com/coding-kelps/liaisons-preprocess) (the preprocessing of the original IBM dataset) |
| - [liaisons-experiments](https://github.com/coding-kelps/liaisons-experiments) (the benchmarking framework that the sample is intended to be used with) |
| - [liaisons-experiments-results](https://huggingface.co/datasets/coding-kelps/liaisons-experiments-results) (the obtained results with this benchmarking) |
| - [mantis-shrimp](https://github.com/coding-kelps/mantis-shrimp) (the configuration-as-code used to set up my workstation for this project) |
|
|
| ## Licensing Information |
|
|
| This work includes data from the following sources: |
|
|
| * Wikipedia content licensed under CC BY-SA 3.0: [Wikipedia](https://en.wikipedia.org/wiki/Wikipedia:Copyrights#Reusers.27_rights_and_obligations) |
| * IBM content licensed under CC BY-SA 3.0: (c) Copyright IBM 2014. Released under [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) |
|
|
| Modifications and preprocessing have been made to the original data. This derivative work is licensed under the same CC BY-SA 3.0 license. |
|
|
| ## Credits |
|
|
| Further information about the original dataset can be found on its original [HuggingFace page](https://huggingface.co/datasets/ibm/claim_stance) and its associated research papers: [Stance Classification of Context-Dependent Claims](https://aclanthology.org/E17-1024/) and [Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization](https://aclanthology.org/W17-5104/). |
|
|
| ## Special Thanks |
|
|
| I would like to credits [Andrew Ireland](http://www.macs.hw.ac.uk/~air/), my supervisor for this project. |
|
|
| <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/0/03/Heriot-Watt_University_logo.svg/1200px-Heriot-Watt_University_logo.svg.png" width="300"> |
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