{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interpretable-semantic-textual-similarity","title":"Interpretable Semantic Textual Similarity: Finding and explaining differences between sentences","arxiv_id":"1612.04868","date":"2016-12-14","proceeding":null,"authors":["I. Lopez-Gazpio","M. Maritxalar","A. Gonzalez-Agirre","G. Rigau","L. Uria","E. Agirre"],"abstract":"User acceptance of artificial intelligence agents might depend on their\nability to explain their reasoning, which requires adding an interpretability\nlayer that fa- cilitates users to understand their behavior. This paper focuses\non adding an in- terpretable layer on top of Semantic Textual Similarity (STS),\nwhich measures the degree of semantic equivalence between two sentences. The\ninterpretability layer is formalized as the alignment between pairs of segments\nacross the two sentences, where the relation between the segments is labeled\nwith a relation type and a similarity score. We present a publicly available\ndataset of sentence pairs annotated following the formalization. We then\ndevelop a system trained on this dataset which, given a sentence pair, explains\nwhat is similar and different, in the form of graded and typed segment\nalignments. When evaluated on the dataset, the system performs better than an\ninformed baseline, showing that the dataset and task are well-defined and\nfeasible. Most importantly, two user studies show how the system output can be\nused to automatically produce explanations in natural language. Users performed\nbetter when having access to the explanations, pro- viding preliminary evidence\nthat our dataset and method to automatically produce explanations is useful in\nreal applications.","url_abs":"http://arxiv.org/abs/1612.04868v1","url_pdf":"http://arxiv.org/pdf/1612.04868v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":null,"task_name":"Relation"},{"task_slug":"sts","task_name":"STS"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[{"slug":"interpretable-sts","name":"Interpretable STS","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.04868","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}