WEKO3
アイテム
Reflective action selection based on positive-unlabeled learning and causality detection model
http://hdl.handle.net/10061/0002000108
http://hdl.handle.net/10061/00020001086c6b48ad-48b2-4897-82e8-39976d14f782
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-01-26 | |||||||
| タイトル | ||||||||
| タイトル | Reflective action selection based on positive-unlabeled learning and causality detection model | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Dialogue system | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Ambiguous request | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Reflective action | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Causality | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Sightseeing | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | PU learning | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Label propagation | |||||||
| 資源タイプ | ||||||||
| 資源タイプ | journal article | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| 著者 |
Tanaka, Shohei
× Tanaka, Shohei
× 吉野, 幸一郎× 須藤, 克仁× 中村, 哲 |
|||||||
| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | Task-oriented dialogue systems need to take appropriate actions not only for clear user requests but also for ambiguous and vague ones. In this study, “ambiguous” denotes that although users have potential requests, they failed to clearly define and verbalize their content and conditions which can be associated with system actions. For such ambiguous requests, taking reflective actions is one plausible choice for such systems. In our study, “reflective” denotes taking actions that satisfy user requests before the users themselves clarify their demands. We constructed such a reflective dialogue agent by collecting a corpus that includes pairs of ambiguous user requests and corresponding reflective system actions on sightseeing navigation with a smartphone. Since annotating every possible combination of user requests and system actions is impossible, this study built a corpus where one reflective action is annotated to one ambiguous user request. To train an action selection model on such incomplete training data in which only one action is associated with a request, we applied the positive/unlabeled (PU) learning method, which assumes that only part of the data is labeled with positive examples. In addition, we enhanced the action selection by extracting and distilling knowledge that corresponds to causality from the training data using a causality detection model. The experimental results show that both the PU learning method and the causality detection model improved the performances of the reflective action selection compared to the conventional positive/negative (PN) learning method. | |||||||
| 書誌情報 |
en : Computer Speech & Language 巻 78, 発行日 2022-10-15 |
|||||||
| artnum | ||||||||
| 値 | 101463 | |||||||
| 出版者 | ||||||||
| 出版者 | Elsevier | |||||||
| ISSN | ||||||||
| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 0885-2308 | |||||||
| 出版者版DOI | ||||||||
| 関連タイプ | isReplacedBy | |||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.1016/j.csl.2022.101463 | |||||||
| 出版者版URI | ||||||||
| 関連タイプ | isReplacedBy | |||||||
| 識別子タイプ | URI | |||||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S0885230822000869 | |||||||
| 権利 | ||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by/4.0 | |||||||
| 権利情報 | c 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | |||||||
| 著者版フラグ | ||||||||
| 出版タイプ | NA | |||||||
