{"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/temporal-and-aspectual-entailment","title":"Temporal and Aspectual Entailment","arxiv_id":"1904.01297","date":"2019-04-02","proceeding":"WS 2019 5","authors":["Thomas Kober","Sander Bijl de Vroe","Mark Steedman"],"abstract":"Inferences regarding \"Jane's arrival in London\" from predications such as\n\"Jane is going to London\" or \"Jane has gone to London\" depend on tense and\naspect of the predications. Tense determines the temporal location of the\npredication in the past, present or future of the time of utterance. The\naspectual auxiliaries on the other hand specify the internal constituency of\nthe event, i.e. whether the event of \"going to London\" is completed and whether\nits consequences hold at that time or not. While tense and aspect are among the\nmost important factors for determining natural language inference, there has\nbeen very little work to show whether modern NLP models capture these semantic\nconcepts. In this paper we propose a novel entailment dataset and analyse the\nability of a range of recently proposed NLP models to perform inference on\ntemporal predications. We show that the models encode a substantial amount of\nmorphosyntactic information relating to tense and aspect, but fail to model\ninferences that require reasoning with these semantic properties.","url_abs":"http://arxiv.org/abs/1904.01297v1","url_pdf":"http://arxiv.org/pdf/1904.01297v1.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":"temporal-and-aspectual-entailment","repo_url":"https://github.com/tttthomasssss/iwcs2019","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"temporal-and-aspectual-entailment","repo_url":"https://github.com/MindCode-4/code-13/tree/main/temporal-and-aspectual-entailment","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"temporal-and-aspectual-entailment","repo_url":"https://github.com/MindCode-4/code-9/tree/main/temporal-and-aspectual-entailment","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"temporal-and-aspectual-entailment","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/3/temporal-and-aspectual-entailment","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"temporal-and-aspectual-entailment","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/4/temporal-and-aspectual-entailment","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1904.01297","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}