https://scholars.lib.ntu.edu.tw/handle/123456789/580911
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Su F.-G | en_US |
dc.contributor.author | Hsu A.R | en_US |
dc.contributor.author | Tuan Y.-L | en_US |
dc.contributor.author | HUNG-YI LEE | en_US |
dc.creator | Su F.-G;Hsu A.R;Tuan Y.-L;Lee H.-Y. | - |
dc.date.accessioned | 2021-09-02T00:05:15Z | - |
dc.date.available | 2021-09-02T00:05:15Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 2308457X | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094428911&doi=10.21437%2fInterspeech.2019-1696&partnerID=40&md5=ddc3bf4d0aae0cbbd1bebb49b2798889 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/580911 | - |
dc.description.abstract | Personalized responses are essential for having an informative and human-like conversation. Because it is difficult to collect a large amount of dialogues involved with specific speakers, it is desirable that chatbot can learn to generate personalized responses simply from monologues of individuals. In this paper, we propose a novel personalized dialogue generation method which reduces the training data requirement to dialogues without speaker information and monologues of every target speaker. In the proposed approach, a generative adversarial network ensures the responses containing recognizable personal characteristics of the target speaker, and a backward SEQ2SEQ model reconstructs the input message for keeping the coherence of the generated responses. The proposed model demonstrates its flexibility to respond to open-domain conversations, and the experimental results show that the proposed method performs favorably against prior work in coherence, personality classification, and human evaluation. Copyright ? 2019 ISCA | - |
dc.relation.ispartof | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH | - |
dc.title | Personalized dialogue response generation learned from monologues | en_US |
dc.type | conference paper | en |
dc.identifier.doi | 10.21437/Interspeech.2019-1696 | - |
dc.identifier.scopus | 2-s2.0-85094428911 | - |
dc.relation.pages | 4160-4164 | - |
dc.relation.journalvolume | 2019-September | - |
item.openairetype | conference paper | - |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Intel-NTU Connected Context Computing Center | - |
crisitem.author.dept | Communication Engineering | - |
crisitem.author.dept | Computer Science and Information Engineering | - |
crisitem.author.dept | Networking and Multimedia | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.dept | Master's Program in Smart Medicine and Health Informatics (SMARTMHI) | - |
crisitem.author.orcid | 0000-0002-9654-5747 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | Others: International Research Centers | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | International College | - |
顯示於: | 電機工程學系 |
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