{"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/supervised-learning-of-universal-sentence","title":"Supervised Learning of Universal Sentence Representations from Natural Language Inference Data","arxiv_id":"1705.02364","date":"2017-05-05","proceeding":"EMNLP 2017 9","authors":["Alexis Conneau","Douwe Kiela","Holger Schwenk","Loic Barrault","Antoine Bordes"],"abstract":"Many modern NLP systems rely on word embeddings, previously trained in an\nunsupervised manner on large corpora, as base features. Efforts to obtain\nembeddings for larger chunks of text, such as sentences, have however not been\nso successful. Several attempts at learning unsupervised representations of\nsentences have not reached satisfactory enough performance to be widely\nadopted. In this paper, we show how universal sentence representations trained\nusing the supervised data of the Stanford Natural Language Inference datasets\ncan consistently outperform unsupervised methods like SkipThought vectors on a\nwide range of transfer tasks. Much like how computer vision uses ImageNet to\nobtain features, which can then be transferred to other tasks, our work tends\nto indicate the suitability of natural language inference for transfer learning\nto other NLP tasks. 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