--- 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.