{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00208179","sets":["1164:5159:10092:10413"]},"path":["10413"],"owner":"44499","recid":"208179","title":["疑似負例を用いたData-to-Textモデルの学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-11-25"},"_buckets":{"deposit":"13c2edac-8fa4-40da-92c8-88995879c7fe"},"_deposit":{"id":"208179","pid":{"type":"depid","value":"208179","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"疑似負例を用いたData-to-Textモデルの学習","author_link":["521052","521057","521065","521056","521060","521051","521061","521054","521062","521059","521063","521064","521066","521058","521053","521055"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"疑似負例を用いたData-to-Textモデルの学習"},{"subitem_title":"Learning with Contrastive Examples for Data-to-Text Generation","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"言い換え・文生成・要約","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2020-11-25","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"産業技術総合研究所"},{"subitem_text_value":"産業技術総合研究所"},{"subitem_text_value":"お茶の水女子大学"},{"subitem_text_value":"産業技術総合研究所"},{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"お茶の水女子大学／産業技術総合研究所"},{"subitem_text_value":"東京大学／産業技術総合研究所"},{"subitem_text_value":"東京工業大学／産業技術総合研究所"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing 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由衣"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"石垣, 達也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"青木, 花純"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"能地, 宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"五島, 圭一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小林, 一郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"宮尾, 祐介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高村, 大也"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yui, Uehara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tatsuya, Ishigaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kasumi, Aoki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroshi, Noji","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Keiichi, Goshima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ichiro, Kobayashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yusuke, Miyao","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroya, Takamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10442647","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8663","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では，日経平均データなどの時系列数値データを入力とし，その値動きを説明する市況テキストを出力するdata-to-text課題を扱う．従来，data-to-textモデルは時系列数値データと正解テキストの対を用いて学習される．既存モデルによる生成文は，例えば「日経平均，続落」を出力すべき入力に対し，「日経平均，反発」と出力するなど，値動きを表す重要語について致命的なエラーを含むことがある．本研究では，このようなエラーを軽減し生成文の正しさを向上させる目的で，正解文だけでなく間違いを含む文を疑似負例として自動生成し学習時に活用する枠組みを提案する．疑似負例は「続落」「反発」といった値動きを表現する語をあらかじめ定義し，正解文中の重要語を別の重要語で置き換えることで自動生成する．疑似負例の活用によるエラー削減の効果について，疑似負例の種類，および学習時に用いる損失関数という2つの観点から分析する．実験より，1)疑似負例の活用により生成文の流暢性を失うことなく正しさが向上する，2)重視する性能指標によって選択すべき損失関数は異なる，3)特定の規則により生成した疑似負例はより効果的に正しさの向上に寄与する，という3つの知見が得られた．また，人間による評価においても，負例の活用が生成文の正しさの向上に寄与することが確かめられた．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告音声言語情報処理（SLP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2020-11-25","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"30","bibliographicVolumeNumber":"2020-SLP-134"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":208179,"updated":"2025-01-19T18:53:50.210462+00:00","links":{},"created":"2025-01-19T01:09:45.675919+00:00"}