{"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/neural-sentence-embedding-using-only-in","title":"Neural Sentence Embedding using Only In-domain Sentences for Out-of-domain Sentence Detection in Dialog Systems","arxiv_id":"1807.11567","date":"2018-07-27","proceeding":null,"authors":["Seonghan Ryu","Seokhwan Kim","Junhwi Choi","Hwanjo Yu","Gary Geunbae Lee"],"abstract":"To ensure satisfactory user experience, dialog systems must be able to\ndetermine whether an input sentence is in-domain (ID) or out-of-domain (OOD).\nWe assume that only ID sentences are available as training data because\ncollecting enough OOD sentences in an unbiased way is a laborious and\ntime-consuming job. This paper proposes a novel neural sentence embedding\nmethod that represents sentences in a low-dimensional continuous vector space\nthat emphasizes aspects that distinguish ID cases from OOD cases. We first used\na large set of unlabeled text to pre-train word representations that are used\nto initialize neural sentence embedding. Then we used domain-category analysis\nas an auxiliary task to train neural sentence embedding for OOD sentence\ndetection. After the sentence representations were learned, we used them to\ntrain an autoencoder aimed at OOD sentence detection. We evaluated our method\nby experimentally comparing it to the state-of-the-art methods in an\neight-domain dialog system; our proposed method achieved the highest accuracy\nin all tests.","url_abs":"http://arxiv.org/abs/1807.11567v1","url_pdf":"http://arxiv.org/pdf/1807.11567v1.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":"neural-sentence-embedding-using-only-in","repo_url":"https://github.com/BevoLEt/Neural-sentence-embedding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"neural-sentence-embedding-using-only-in","repo_url":"https://github.com/BevoLEt/Sentence-Decetion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embedding","task_name":"Sentence Embedding"},{"task_slug":"sentence-embedding-1","task_name":"Sentence-Embedding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.11567","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}