著者

Nurul Lubis Sakriani Sakti Koichiro Yoshino Satoshi Nakamura

出版者

The Japanese Society for Artificial Intelligence

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.33, no.1, pp.DSH-D_1-10, 2018-01-01 (Released:2018-01-31)

参考文献数

29

被引用文献数

2

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To completely mimic the naturalness of human interaction in Human-Computer Interaction (HCI), emotion is an essential aspect that should not be overlooked. Emotion allows for a rich and meaningful human interaction. In communicating, not only we express our emotional state, but we are also affected by our conversational counterpart. However, existing works have largely focused only on occurrences of emotion through recognition and simulation. The relationship between an utterance of a speaker and the resulting emotional response that it triggers is not yet closely examined. Observation and incorporation of the underlying process that causes change of emotion can provide useful information for dialogue systems in making a more emotionally intelligent decision, such as being able to take proper action with regard to user’s emotion, and to be aware of the emotional implication of their response. To bridge this gap, in this paper, we tackle three main tasks: 1) recognition of emotional states, 2) analysis of social-affective events in spontaneous conversational data, to capture the relationship between actions taken in discourse and the emotional response that follows, and 3) prediction of emotional triggers and responses in a conversational context. The proposed study differs from existing works in that it focuses on the change of emotion (emotional response) and its cause (emotional triggers) on top of the occurrence of emotion itself. The analysis and experimental results are reported in detail in this paper, showing promising initial results for future works and development.

著者

水上 雅博 Lasguido Nio 木付 英士 野村 敏男 Graham Neubig 吉野 幸一郎 Sakriani Sakti 戸田 智基 中村 哲

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

pp.DSF-517, (Released:2015-12-15)

参考文献数

23

被引用文献数

4

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In dialogue systems, dialogue modeling is one of the most important factors contributing to user satisfaction. Especially in example-based dialogue modeling (EBDM), effective methods for dialog example databases and selecting response utterances from examples improve dialogue quality. Conventional EBDM-based systems use example database consisting of pair of user query and system response. However, the best responses for the same user query are different depending on the user's preference. We propose an EBDM framework that predicts user satisfaction to select the best system response for the user from multiple response candidates. We define two methods for user satisfaction prediction; prediction using user query and system response pairs, and prediction using user feedback for the system response. Prediction using query/response pairs allows for evaluation of examples themselves, while prediction using user feedback can be used to adapt the system responses to user feedback. We also propose two response selection methods for example-based dialog, one static and one user adaptive, based on these satisfaction prediction methods. Experimental results showed that the proposed methods can estimate user satisfaction and adapt to user preference, improving user satisfaction score.

著者

The Tung Nguyen Koichiro Yoshino Sakriani Sakti Satoshi Nakamura

出版者

The Japanese Society for Artificial Intelligence

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.35, no.1, pp.DSI-C_1-12, 2020-01-01 (Released:2020-01-01)

参考文献数

24

被引用文献数

2

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In the past few years, there has been an increasing number of works on negotiation dialog. These studies mainly focus on situations where interlocutors work cooperatively to agree on a mutual objective that can fulfill each of their own requirements. However, in real-life negotiation, such situations do not happen all the time, and participants can tell lies to gain an advantage. In this research, we propose a negotiation dialog management system that detects when a user is lying and a dialog behavior for how the system should react when faced with a lie. We design our system for a living habits consultation scenario, where the system tries to persuade users to adopt healthy living habits. We show that we can use the partially observable Markov decision process (POMDP) to model this conversation and use reinforcement learning to train the system’s policy. Our experimental results demonstrate that the dialog manager considering deceptive states outperformed a dialog manager without this consideration in terms of the accuracy of action selection, and improved the true success rate of the negotiation in the healthcare consultation domain.

著者

赤部 晃一 Graham Neubig Sakriani Sakti 戸田 智基 中村 哲

出版者

一般社団法人 言語処理学会

雑誌

自然言語処理 (ISSN:13407619)

巻号頁・発行日

vol.23, no.1, pp.87-117, 2016-01-25 (Released:2016-04-25)

参考文献数

30

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複雑化する機械翻訳システムを比較し,問題点を把握・改善するため,誤り分析が利用される.その手法として,様々なものが提案されているが,多くは単純にシステムの翻訳結果と正解訳の差異に着目して誤りを分類するものであり,人手による分析への活用を目的とするものではなかった.本研究では,人手による誤り分析を効率化する手法として,機械学習の枠組みを導入した誤り箇所選択手法を提案する.学習によって評価の低い訳出と高い訳出を分類するモデルを作成し,評価低下の手がかりを自動的に獲得することで,人手による誤り分析の効率化を図る.実験の結果,提案法を活用することで,人手による誤り分析の効率が向上した.

著者

角森 唯子 Graham Neubig Sakriani Sakti 平岡 拓也 水上 雅博 戸田 智基 中村 哲

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会研究会資料 言語・音声理解と対話処理研究会 75回 (2015/10) (ISSN:09185682)

巻号頁・発行日

pp.04, 2015-10-26 (Released:2021-06-28)

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When humans attempt to detect deception, they perform two actions: looking for telltale signs of deception, and asking questions to attempt to unveil a deceptive conversational partner. There has been a significant amount of prior work on automatic deception detection, which focuses on the former. On the other hand, we focus on the latter, constructing a dialog system for an interview task that acts as an interviewer asking questions to attempt to catch a potentially deceptive interviewee. We propose several dialog strategies for this system, and measure the utterance-level deception detection accuracy of each, finding that a more intelligent dialog strategy results in slightly better deception detection accuracy.