{"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/multi-turn-inference-matching-network-for","title":"Multi-turn Inference Matching Network for Natural Language Inference","arxiv_id":"1901.02222","date":"2019-01-08","proceeding":null,"authors":["Chunhua Liu","Shan Jiang","Hainan Yu","Dong Yu"],"abstract":"Natural Language Inference (NLI) is a fundamental and challenging task in\nNatural Language Processing (NLP). Most existing methods only apply one-pass\ninference process on a mixed matching feature, which is a concatenation of\ndifferent matching features between a premise and a hypothesis. In this paper,\nwe propose a new model called Multi-turn Inference Matching Network (MIMN) to\nperform multi-turn inference on different matching features. In each turn, the\nmodel focuses on one particular matching feature instead of the mixed matching\nfeature. To enhance the interaction between different matching features, a\nmemory component is employed to store the history inference information. The\ninference of each turn is performed on the current matching feature and the\nmemory. We conduct experiments on three different NLI datasets. The\nexperimental results show that our model outperforms or achieves the\nstate-of-the-art performance on all the three datasets.","url_abs":"http://arxiv.org/abs/1901.02222v1","url_pdf":"http://arxiv.org/pdf/1901.02222v1.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":"multi-turn-inference-matching-network-for","repo_url":"https://github.com/blcunlp/RTE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}