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Open AccessArticle

Knowledge-Grounded Chatbot Based on Dual Wasserstein Generative Adversarial Networks with Effective Attention Mechanisms

1
Computer and Communications Engineering, Kangwon National University, Chuncheon 24341, Korea
2
Language Intelligence Research Lab., Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
3
Computer Science and Engineering, Konkuk University, Seoul 05029, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(9), 3335; https://doi.org/10.3390/app10093335
Received: 16 April 2020 / Revised: 5 May 2020 / Accepted: 8 May 2020 / Published: 11 May 2020
(This article belongs to the Special Issue Machine Learning and Natural Language Processing)
A conversation is based on internal knowledge that the participants already know or external knowledge that they have gained during the conversation. A chatbot that communicates with humans by using its internal and external knowledge is called a knowledge-grounded chatbot. Although previous studies on knowledge-grounded chatbots have achieved reasonable performance, they may still generate unsuitable responses that are not associated with the given knowledge. To address this problem, we propose a knowledge-grounded chatbot model that effectively reflects the dialogue context and given knowledge by using well-designed attention mechanisms. The proposed model uses three kinds of attention: Query-context attention, query-knowledge attention, and context-knowledge attention. In our experiments with the Wizard-of-Wikipedia dataset, the proposed model showed better performances than the state-of-the-art model in a variety of measures. View Full-Text
Keywords: knowledge-grounded chatbot; multi-turn chatbot; context-knowledge attention mechanism knowledge-grounded chatbot; multi-turn chatbot; context-knowledge attention mechanism
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Kim, S.; Kwon, O.-W.; Kim, H. Knowledge-Grounded Chatbot Based on Dual Wasserstein Generative Adversarial Networks with Effective Attention Mechanisms. Appl. Sci. 2020, 10, 3335.

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