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

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

Hiroaki Tanaka Yu Suzuki Shotaro Yamasaki Koichiro Yoshino Ko Kato Satoshi Nakamura

出版者

Information Processing Society of Japan

雑誌

IPSJ Transactions on Bioinformatics (ISSN:18826679)

巻号頁・発行日

vol.11, pp.14-23, 2018 (Released:2018-07-05)

参考文献数

43

被引用文献数

1

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Protein production in plants is a hot topic because there are many benefits relative to bacteria, yeasts, and animals, but the amount of protein expression in plants is less. It is argued that editing 5'UTRs increases the amount of translated proteins. However, obtaining such 5'UTRs is difficult due to the cost, time and effort required in experiments. To solve this, we predict the amount of translated proteins by machine learning. In this paper, we propose a method, named “R-STEINER, ” that generates 5'UTRs that increase the amount of proteins of a given gene. The proposed process involves building a model for predicting the amount of translated proteins, generating 5'UTRs, selecting them and increasing the proteins according to the model. This method enables us to obtain 5'UTRs that increase the amount of translated proteins without real synthesis experiments, resulting in reduced cost, time and effort. In our study, we built a prediction model for Oryza sativa and synthesized the 5'UTRs generated by R-STEINER. We confirmed that the model can predict the amount of translated proteins with a correlation coefficient of 0.89.

著者

Seiya Kawano 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.36, no.4, pp.E-KC9_1-14, 2021-07-01 (Released:2021-07-01)

参考文献数

48

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Building a controllable neural conversation model (NCM) is an important task. In this paper, we focus on controlling the responses of NCMs using dialogue act labels of responses as conditions. We introduce a reinforcement learning framework involving adversarial learning for conditional response generation. Our proposed method has a new label-aware objective that encourages the generation of discriminative responses by the given dialogue act label while maintaining the naturalness of the generated responses. We compared the proposed method with conventional methods that generate conditional responses. The experimental results showed that our proposed method has higher controllability conditioned by the dialogue acts even though it has higher or comparable naturalness to the conventional models.

著者

Ikuo KESHI Yu SUZUKI Koichiro YOSHINO Satoshi NAKAMURA

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE Transactions on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E101.D, no.4, pp.1066-1078, 2018-04-01 (Released:2018-04-01)

参考文献数

24

被引用文献数

1

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The problem with distributed representations generated by neural networks is that the meaning of the features is difficult to understand. We propose a new method that gives a specific meaning to each node of a hidden layer by introducing a manually created word semantic vector dictionary into the initial weights and by using paragraph vector models. We conducted experiments to test the hypotheses using a single domain benchmark for Japanese Twitter sentiment analysis and then evaluated the expandability of the method using a diverse and large-scale benchmark. Moreover, we tested the domain-independence of the method using a Wikipedia corpus. Our experimental results demonstrated that the learned vector is better than the performance of the existing paragraph vector in the evaluation of the Twitter sentiment analysis task using the single domain benchmark. Also, we determined the readability of document embeddings, which means distributed representations of documents, in a user test. The definition of readability in this paper is that people can understand the meaning of large weighted features of distributed representations. A total of 52.4% of the top five weighted hidden nodes were related to tweets where one of the paragraph vector models learned the document embeddings. For the expandability evaluation of the method, we improved the dictionary based on the results of the hypothesis test and examined the relationship of the readability of learned word vectors and the task accuracy of Twitter sentiment analysis using the diverse and large-scale benchmark. We also conducted a word similarity task using the Wikipedia corpus to test the domain-independence of the method. We found the expandability results of the method are better than or comparable to the performance of the paragraph vector. Also, the objective and subjective evaluation support each hidden node maintaining a specific meaning. Thus, the proposed method succeeded in improving readability.