Explaining Interactions Between Text Spans

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Documents

  • Fulltext

    Final published version, 903 KB, PDF document

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).
Original languageEnglish
Title of host publicationProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics (ACL)
Publication date2023
Pages12709-12730
ISBN (Print)N 979-8-89176-060-8
DOIs
Publication statusPublished - 2023
Event2023 Conference on Empirical Methods in Natural Language Processing - Singapore
Duration: 6 Dec 202310 Dec 2023

Conference

Conference2023 Conference on Empirical Methods in Natural Language Processing
BySingapore
Periode06/12/202310/12/2023

ID: 381512104