DC Field | Value | Language |
---|---|---|
dc.contributor.author | 방지수 | - |
dc.date.accessioned | 2022-10-31T16:31:32Z | - |
dc.date.available | 2022-10-31T16:31:32Z | - |
dc.date.issued | 2021 | - |
dc.identifier.other | OAK-2015-09611 | - |
dc.identifier.uri | http://postech.dcollection.net/common/orgView/200000506982 | ko_KR |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/114158 | - |
dc.description | Doctor | - |
dc.description.abstract | A dialog system is a system that enables humans and machines to communicate in natural language. Dialogue systems are largely divided into a task-oriented dialogue system and a chat-oriented dialogue system. The task-oriented dialogue system has a goal to accomplish a specific task, such as plane ticket booking, restaurant information providing, or bus route information providing. The task-oriented dialogue system has proven its usefulness in the industry in narrow domains but is not adaptable for providing natural conversation experience to humans in open-domain situations. On the other hand, the chat-oriented dialogue system aims to have a natural conversation with the user. The chat-oriented dialogue system does not have a specific task to accomplish but has a goal to act like a human. Although listening to a conversation partner is a key factor in the success of dialogue systems or conversational agents, recent neural conversation systems have no interest in generating listening-oriented responses. In this paper, we conduct research on a dialogue system that provides responses that make users feel listened to. In detail, we propose an end-to-end dialogue system that generates listening-oriented responses which make users reveal themselves and feel positive emotions. The proposed model uses `self-disclosure' and `positiveness' as listening features and generates responses appropriately to the features. Furthermore, the model infers a user response that will be brought out at the end of the dialogue and uses the inferred user response for generating a system response. By utilizing both listening features and user responses, our model becomes capable of generating listening-oriented responses. In quantitative and qualitative experiments, our model shows that it can generate listening-oriented responses that induce users to disclose themselves and talk positively. The results also show that the model utilizing user responses generates more listening-oriented responses than the models not using user responses. | - |
dc.language | eng | - |
dc.publisher | 포항공과대학교 | - |
dc.title | Listening-Oriented Response Generation by Exploiting User Responses | - |
dc.title.alternative | 사용자 답변을 활용한 경청 지향 대화 응답 생성 | - |
dc.type | Thesis | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.date.degree | 2021- 8 | - |
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