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--- |
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license: mit |
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task_categories: |
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- text-classification |
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language: |
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- en |
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tags: |
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- 'rationale-extraction' |
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- reasoning |
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- nli |
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- fact-checking |
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- explainability |
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pretty_name: spanex |
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size_categories: |
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- 1K<n<10K |
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|
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configs: |
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- config_name: snli_extended |
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data_files: |
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- split: test |
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path: snli_extended.jsonl |
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- config_name: fever_extended |
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data_files: |
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- split: test |
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path: fever_extended.jsonl |
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- config_name: snli |
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data_files: |
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- split: test |
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path: snli.jsonl |
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- config_name: fever |
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data_files: |
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- split: test |
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path: fever.jsonl |
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--- |
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|
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SpanEx consists of 7071 instances annotated for span interactions. |
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SpanEx is the first dataset with human phrase-level interaction explanations with explicit labels for interaction types. |
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Moreover, SpanEx is annotated by three annotators, which opens new avenues for studies of human explanation agreement -- an understudied area in the explainability literature. |
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Our study reveals that while human annotators often agree on span interactions, they also offer complementary reasons for a prediction, collectively providing a comprehensive set of reasons for a prediction. |
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We collect explanations of span interactions for NLI on the SNLI dataset and for FC on the FEVER dataset. |
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|
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Please cite the following paper if you use this dataset: |
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|
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``` |
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@inproceedings{choudhury-etal-2023-explaining, |
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title = "Explaining Interactions Between Text Spans", |
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author = "Choudhury, Sagnik and |
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Atanasova, Pepa and |
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Augenstein, Isabelle", |
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editor = "Bouamor, Houda and |
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Pino, Juan and |
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Bali, Kalika", |
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booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2023", |
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address = "Singapore", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.emnlp-main.783", |
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doi = "10.18653/v1/2023.emnlp-main.783", |
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pages = "12709--12730", |
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abstract = "Reasoning over spans of tokens from different parts of the input is essential for natural language understanding (NLU) tasks such as fact-checking (FC), machine reading comprehension (MRC) or natural language inference (NLI). However, existing highlight-based explanations primarily focus on identifying individual important features or interactions only between adjacent tokens or tuples of tokens. Most notably, there is a lack of annotations capturing the human decision-making process with respect to the necessary interactions for informed decision-making in such tasks. To bridge this gap, we introduce SpanEx, a multi-annotator dataset of human span interaction explanations for two NLU tasks: NLI and FC. We then investigate the decision-making processes of multiple fine-tuned large language models in terms of the employed connections between spans in separate parts of the input and compare them to the human reasoning processes. Finally, we present a novel community detection based unsupervised method to extract such interaction explanations. We make the code and the dataset available on [Github](https://github.com/copenlu/spanex). The dataset is also available on [Huggingface datasets](https://huggingface.co/datasets/copenlu/spanex).", |
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} |
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``` |
