{"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/natural-language-inference-over-interaction-1","title":"Natural Language Inference over Interaction Space","arxiv_id":"1709.04348","date":"2017-09-13","proceeding":"ICLR 2018 1","authors":["Yichen Gong","Heng Luo","Jian Zhang"],"abstract":"Natural Language Inference (NLI) task requires an agent to determine the\nlogical relationship between a natural language premise and a natural language\nhypothesis. We introduce Interactive Inference Network (IIN), a novel class of\nneural network architectures that is able to achieve high-level understanding\nof the sentence pair by hierarchically extracting semantic features from\ninteraction space. We show that an interaction tensor (attention weight)\ncontains semantic information to solve natural language inference, and a denser\ninteraction tensor contains richer semantic information. One instance of such\narchitecture, Densely Interactive Inference Network (DIIN), demonstrates the\nstate-of-the-art performance on large scale NLI copora and large-scale NLI\nalike corpus. It's noteworthy that DIIN achieve a greater than 20% error\nreduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to\nthe strongest published system.","url_abs":"http://arxiv.org/abs/1709.04348v2","url_pdf":"http://arxiv.org/pdf/1709.04348v2.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":"natural-language-inference-over-interaction-1","repo_url":"https://github.com/YichenGong/Densely-Interactive-Inference-Network","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"natural-language-inference-over-interaction-1","repo_url":"https://github.com/YerevaNN/DIIN-in-Keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"paraphrase-identification","task_name":"Paraphrase Identification"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/natural-language-inference-on-snli","task":"Natural Language Inference","dataset":"SNLI","model":"448D Densely Interactive Inference Network (DIIN, code) Ensemble","rank_in_archive_order":27,"of":98,"metrics":{"% Test Accuracy":"88.9","% Train Accuracy":"92.3","Parameters":"17m"},"uses_additional_data":false},{"leaderboard":"/sota/natural-language-inference-on-snli","task":"Natural Language Inference","dataset":"SNLI","model":"448D Densely Interactive Inference Network (DIIN, code)","rank_in_archive_order":40,"of":98,"metrics":{"% Test Accuracy":"88.0","% Train Accuracy":"91.2","Parameters":"4.4m"},"uses_additional_data":false},{"leaderboard":"/sota/paraphrase-identification-on-quora-question","task":"Paraphrase Identification","dataset":"Quora Question Pairs","model":"DIIN","rank_in_archive_order":20,"of":31,"metrics":{"Accuracy":"89.06"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.04348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.04348"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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