{"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/sentence-pair-scoring-towards-unified","title":"Sentence Pair Scoring: Towards Unified Framework for Text Comprehension","arxiv_id":"1603.06127","date":"2016-03-19","proceeding":null,"authors":["Petr Baudiš","Jan Pichl","Tomáš Vyskočil","Jan Šedivý"],"abstract":"We review the task of Sentence Pair Scoring, popular in the literature in\nvarious forms - viewed as Answer Sentence Selection, Semantic Text Scoring,\nNext Utterance Ranking, Recognizing Textual Entailment, Paraphrasing or e.g. a\ncomponent of Memory Networks.\n  We argue that all such tasks are similar from the model perspective and\npropose new baselines by comparing the performance of common IR metrics and\npopular convolutional, recurrent and attention-based neural models across many\nSentence Pair Scoring tasks and datasets. We discuss the problem of evaluating\nrandomized models, propose a statistically grounded methodology, and attempt to\nimprove comparisons by releasing new datasets that are much harder than some of\nthe currently used well explored benchmarks. We introduce a unified open source\nsoftware framework with easily pluggable models and tasks, which enables us to\nexperiment with multi-task reusability of trained sentence model. We set a new\nstate-of-art in performance on the Ubuntu Dialogue dataset.","url_abs":"http://arxiv.org/abs/1603.06127v4","url_pdf":"http://arxiv.org/pdf/1603.06127v4.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":[{"paper_slug":"sentence-pair-scoring-towards-unified","repo_url":"https://github.com/brmson/dataset-sts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1603.06127","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}