| Project/Area Number |
18K11435
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Osaka University |
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Principal Investigator |
Arase Yuki 大阪大学, 情報科学研究科, 准教授 (00747165)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2019: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 対話システム / 応答生成 / ニューラル対話モデル / 対話コーパス / 語用論的言い換え / 自然言語処理 / 対話破綻検出 / スタイル変換 / 言語資源 |
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| Outline of Final Research Achievements |
Neural conversation models based on deep neural networks have significantly improved fluency in response generation. However, these fluent responses are not necessarily satisfactory. Previous studies have revealed that automatically generated responses would break down a conversation between a user and system. Furthermore, even though these automatic responses are acceptable, they tend to be less attractive to users and may degrade user engagements. To address these problems, we developed novel neural conversation models that are sensitive to users’ utterances and generate meaningful responses. Further, we created a conversation corpus that opens up a new door to the conversational system’s research. It contributes to advancing natural language understanding technology to infer users’ hidden intents from their indirect utterances.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で開発したユーザの発話内容に則しつつ情報量の多い応答を生成できるモデルは、これまで当該研究分野で広く認識されていた問題を解決するものであり、学術的だけでなく産業的貢献も大きい。また本研究で構築した対話コーパスは、これまで重要性を認知されながら手つかずであった、ユーザ発話に隠された言外の意図の推定を可能とするものであり、対話システム研究に新たな扉を開く、顕著な学術的貢献を持つ。
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