| 研究課題/領域番号 |
19K12132
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分61030:知能情報学関連
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| 研究機関 | 大学共同利用機関法人情報・システム研究機構(機構本部施設等) |
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研究代表者 |
金 進東 大学共同利用機関法人情報・システム研究機構(機構本部施設等), データサイエンス共同利用基盤施設, 特任准教授 (40536893)
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| 研究期間 (年度) |
2019-04-01 – 2024-03-31
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| 研究課題ステータス |
交付 (2022年度)
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| 配分額 *注記 |
4,420千円 (直接経費: 3,400千円、間接経費: 1,020千円)
2021年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2020年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2019年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
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| キーワード | dialog / intelligent agent / natural language query / database search / task-oriented dialog / intent detection / dialog agent / intelligent interface / agent / search / question answering / data science |
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| 研究開始時の研究の概要 |
Data science is becoming a new paradigm of science, and a lot of investment has been made to develop science data. However, scientists are often unaware of how to access science data. Meanwhile, there has been increasing interest in the technology of conversational agent (CA), which can talk with users in human language, helping them accomplish certain tasks.
The research is to investigate the potential of CA technology for search-oriented dialogs to help scientists access science data. We expect it to contribute to advancing the CA technology, and improving the environment of data science.
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| 研究実績の概要 |
In R4, a thorough evaluation on the search-oriented dialog system was conducted, which revealed several problems in the intent detection system. We conducted research to solve the problems, and found that they could be effectively solved by utilizing few-shot learning with InstructGPT. During the research, we realized that the state-of-the-art of pre-trained language models were rapidly evolving, which we needed to be able to constantly leverage. We made a change to the architecture of the search-oriented dialog system, so that we can easily incorporate external language resources like pre-trained language models. With the new architecture, we could utilize recently released pre-trained language models, including chatGPT and GPT4, which led to much improved performance.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
Due to the pandemic situation the original schedule of the research has been changed substantially. Also, due to problems found during a thorough evaluation of the system, the architecture of the system had to be changed. However, thanks to the extension of the research period which was generously granted by JSPS, solutions to the problems could be found, and the research is approaching to finalization.
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| 今後の研究の推進方策 |
In R5, which is the last year of the project, (1) another round of human evaluation will be conducted; (2) thorough analysis of the evaluation results will be performed, and (3) papers will be published on the results. We are also planning to make the search-oriented dialog system available as a plugin for chatGPT, as a way of inseminating the result of the research.
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