{"created":"2023-05-15T14:24:33.420594+00:00","id":3086,"links":{},"metadata":{"_buckets":{"deposit":"6edd34be-360b-4bdf-85c0-4a81dff4eceb"},"_deposit":{"created_by":3,"id":"3086","owners":[3],"pid":{"revision_id":0,"type":"depid","value":"3086"},"status":"published"},"_oai":{"id":"oai:repository.ninjal.ac.jp:00003086","sets":["320:324"]},"author_link":["10422","10423","10424","10425","10426"],"control_number":"3086","item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-07-09","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"60","bibliographicPageStart":"55","bibliographic_titles":[{"bibliographic_title":"Proceedings of the 5th Workshop on Representation Learning for NLP (RepL4NLP-2020)","bibliographic_titleLang":"en"}]}]},"item_10001_description_19":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_10001_description_25":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_description":"Ochanomizu University","subitem_description_type":"Other"},{"subitem_description":"Microsoft Research","subitem_description_type":"Other"},{"subitem_description":"Kyoto University","subitem_description_type":"Other"},{"subitem_description":"National Institute for Japanese Language and Linguistics","subitem_description_type":"Other"},{"subitem_description":"Ochanomizu University","subitem_description_type":"Other"}]},"item_10001_description_26":{"attribute_name":"抄録(英)","attribute_value_mlt":[{"subitem_description":"We propose an AdversariaL training algorithm for commonsense InferenCE (ALICE). We apply small perturbations to word embeddings and minimize the resultant adversarial risk to regularize the model. We exploit a novel combination of two different approaches to estimate these perturbations: 1) using the true label and 2) using the model prediction. Without relying on any human-crafted features, knowledge bases, or additional datasets other than the target datasets, our model boosts the fine-tuning performance of RoBERTa, achieving competitive results on multiple reading comprehension datasets that require commonsense inference.","subitem_description_type":"Other"}]},"item_10001_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.15084/00003070","subitem_identifier_reg_type":"JaLC"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Association for Computational Linguistics"}]},"item_10001_relation_10":{"attribute_name":"ISBN","attribute_value_mlt":[{"subitem_relation_type":"isPartOf","subitem_relation_type_id":{"subitem_relation_type_id_text":"978-1-952148-15-6","subitem_relation_type_select":"ISBN"}}]},"item_10001_version_type_20":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Pereira, Lis","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"10422","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Liu, Xiaodong","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"10423","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Cheng, Fei","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"10424","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Asahara, Masayuki","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"10425","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kobayashi, Ichiro","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"10426","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2020-12-17"}],"displaytype":"detail","filename":"repl4nlp_2020_55.pdf","filesize":[{"value":"2.0 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"repl4nlp_2020_55.pdf","url":"https://repository.ninjal.ac.jp/record/3086/files/repl4nlp_2020_55.pdf"},"version_id":"59da4fff-4391-4d4f-9dd2-20fba75ad3a1"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Adversarial Training for Commonsense Inference","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Adversarial Training for Commonsense Inference","subitem_title_language":"en"}]},"item_type_id":"10001","owner":"3","path":["324"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2020-12-18"},"publish_date":"2020-12-18","publish_status":"0","recid":"3086","relation_version_is_last":true,"title":["Adversarial Training for Commonsense Inference"],"weko_creator_id":"3","weko_shared_id":-1},"updated":"2023-09-27T01:20:32.154022+00:00"}