{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00194258","sets":["1164:3500:9687:9688"]},"path":["9688"],"owner":"44499","recid":"194258","title":["対話コンテキストを考盧したニューラル通話シーン分割"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-01-31"},"_buckets":{"deposit":"4ef16e26-bb01-49dc-ab84-a2a23c169c54"},"_deposit":{"id":"194258","pid":{"type":"depid","value":"194258","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"対話コンテキストを考盧したニューラル通話シーン分割","author_link":["459378","459376","459379","459382","459384","459380","459373","459381","459383","459375","459374","459377"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"対話コンテキストを考盧したニューラル通話シーン分割"},{"subitem_title":"Call Scene Segmentation based on Neural Networks with Conversational Contexts","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"対話応用","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2019-01-31","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日本電信電話株式会社NTTメディアインテリジェンス研究所"},{"subitem_text_value":"日本電信電話株式会社NTTメディアインテリジェンス研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"NTT Media Intelligence Laboratories","subitem_text_language":"en"},{"subitem_text_value":"NTT Media Intelligence Laboratories","subitem_text_language":"en"},{"subitem_text_value":"NTT Media Intelligence Laboratories","subitem_text_language":"en"},{"subitem_text_value":"NTT Media Intelligence Laboratories","subitem_text_language":"en"},{"subitem_text_value":"NTT Media Intelligence Laboratories","subitem_text_language":"en"},{"subitem_text_value":"NTT Media Intelligence Laboratories","subitem_text_language":"en"}]},"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 file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/194258/files/IPSJ-IFAT19133005.pdf","label":"IPSJ-IFAT19133005.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-IFAT19133005.pdf","filesize":[{"value":"429.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"39"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"b8a6e989-73d5-4dd1-90dd-5b4f97111aab","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"増村, 亮"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田中, 智大"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"安藤, 厚志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"神山, 歩相名"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大庭, 隆伸"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"青野, 裕司"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ryo, Masumura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomohiro, Tanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsushi, Ando","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hosana, Kamiyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takanobu, Oba","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yushi, Aono","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10114171","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":"2188-8884","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"通話シーン分割は，人手で定めたいくつかのシーンに通話全体を自動分割する技術であり，コンタクトセンタにおいて，オペレータの業務支援に応用することが期待される．我々は，コンタクトセンタにおける通話シーンを，オープニング，用件把握，用件対応，カスタマー情報把握，クロージングの 5 つのシーンに定め，教師あり学習のアプローチにより通話シーン分割を実現する．精緻な通話シーン分割を行うためには，オペレータとカスタマーのインタラクシヨンの纏まりをうまく捉えることが不可欠である．そこで本稿では，通話シーン分割の問題を発話単位の系列ラベリング問題として捉え，発話文とオペレータかカスタマーかを表す話者役割ラベルの組の系列情報から通話シーンラベル系列を予測するニューラル通話シーン分割手法を提案する．提案手法では，各発話の文と話者役割ラベルから 「どの役割の話者がどんな内容を話したか」 が埋め込まれた発話ベクトルを構成し，その発話系列をリカレントニューラルネットワークにより捉えることで対話コンテキストを考慮した通話シーン分割を実現する．6 業種のコンタクトセンタの模擬通話データを用いた評価実験において，提案手法の有効性を示す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Call scene segmentation that automatically splits contact center dialogues into several call scenes is useful for constructing operator assist systems. This paper develops call scene segmentation methods based on supervised training by defining call scenes in contact center dialogues as following five tag types : opening, call reason, response, customer confirmation, and closing. It is important to capture conversational contexts between an operator and a customer in the contact center dialogues for performing precise online call scene segmentation. Therefore, this paper proposes neural call scene segmentation methods that can directly estimate a call scene label sequence from an utterance sequence based on sequential labeling approach. The proposed methods can capture long-range conversational contexts by simultaneously dealing with both the sequence of sentences and the sequence of speaker role labels. An experiment using contact center dialogue data sets demonstrates the effectiveness of the proposed methods.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告情報基礎とアクセス技術（IFAT）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-01-31","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2019-IFAT-133"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T23:33:09.523548+00:00","created":"2025-01-19T00:59:19.649305+00:00","links":{},"id":194258}