{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:04:18Z","timestamp":1774368258267,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>In human conversations, due to their personalities in mind, people can easily carry out and maintain the conversations. Giving conversational context with persona information to a chatbot, how to exploit the information to generate diverse and sustainable conversations is still a non-trivial task. Previous work on persona-based conversational models successfully make use of predefined persona information and have shown great promise in delivering more realistic responses. And they all learn with the assumption that given a source input, there is only one target response. However, in human conversations, there are massive appropriate responses to a given input message. In this paper, we propose a memory-augmented architecture to exploit persona information from context and incorporate a conditional variational autoencoder model together to generate diverse and sustainable conversations. We evaluate the proposed model on a benchmark persona-chat dataset. Both automatic and human evaluations show that our model can deliver more diverse and more engaging persona-based responses than baseline approaches.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/721","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"5190-5196","source":"Crossref","is-referenced-by-count":57,"title":["Exploiting Persona Information for Diverse Generation of Conversational Responses"],"prefix":"10.24963","author":[{"given":"Haoyu","family":"Song","sequence":"first","affiliation":[{"name":"Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China"}]},{"given":"Wei-Nan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China"},{"name":"Peng Cheng Laboratory, Shenzhen, China"}]},{"given":"Yiming","family":"Cui","sequence":"additional","affiliation":[{"name":"Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China"},{"name":"Joint Laboratory of HIT and iFLYTEK (HFL), iFLYTEK Research, Beijing, China"}]},{"given":"Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"Joint Laboratory of HIT and iFLYTEK (HFL), iFLYTEK Research, Beijing, China"}]},{"given":"Ting","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China"},{"name":"Peng Cheng Laboratory, Shenzhen, China"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:51:21Z","timestamp":1564285881000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/721"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/721","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}