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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":"人同士の会話において，聞き手は反射的な短い応答によるフィードバックで話し手に明示的 / 暗示的に様々な意図を伝達することが多い．話し手はこれらの意図を認識し，コミュニケーションをより効率的にするために発話計画を変更する．これらの機能は人ーシステム間の会話にも役立つことが期待されるが，人が実際のシステムと対面したときに同様のフィードバックを常に返すとは限らない．そこで，我々の設計したシステムに対するユーザのフィードバック現象を調査し，それらの意図を認識する枠組みを提案した．まず，システムの動作に影響を与えるフィードバックの意図を，ユーザの関心の有無や理解状態，話者交代などの観点から分類した．次に，実システムを用いてユーザの短い応答を収集し，第三者の聞き取りによる意図のラベル付けを行った．さらに，収集したデータをもとに意図の認識モデルの学習を行った．結果として，現状のシステムに対しては質問を意図したフィードバックが多いことが分かり，またその認識に関して提案モデルの有効性が確認できた．","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":"2018-07-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2018-SLP-123"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":190618,"links":{}}