Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Chen, Hongshen"'
Autor:
Chen, Hongsheng
This document only includes an excerpt of the corresponding thesis or dissertation. To request a digital scan of the full text, please contact the Ruth Lilly Medical Library's Interlibrary Loan Department (rlmlill@iu.edu).
Externí odkaz:
https://hdl.handle.net/1805/33454
Autor:
Sun, Weiwei, Cai, Hengyi, Chen, Hongshen, Ren, Pengjie, Chen, Zhumin, de Rijke, Maarten, Ren, Zhaochun
In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balanc
Externí odkaz:
http://arxiv.org/abs/2307.03897
Autor:
Zhang, Gangyi, Gao, Chongming, Lei, Wenqiang, Guo, Xiaojie, Li, Shijun, Chen, Hongshen, Ding, Zhuozhi, Xu, Sulong, Wu, Lingfei
Conversational recommendation systems (CRS) commonly assume users have clear preferences, leading to potential over-filtering of relevant alternatives. However, users often exhibit vague, non-binary preferences. We introduce the Vague Preference Mult
Externí odkaz:
http://arxiv.org/abs/2306.04487
Publikováno v:
Open Geosciences, Vol 15, Iss 1, Pp 778-85 (2023)
This research investigates the dynamic interplay between land use changes and landscape ecological risks in China’s Jiangsu Province, specifically focusing on the Huai River-Gaoyou Lake region. Leveraging multi-temporal remote sensing data from 200
Externí odkaz:
https://doaj.org/article/2a70142f41df4b22abdf7ed7b4ee0494
Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario. In real human dialogue, there are many appropriate responses for
Externí odkaz:
http://arxiv.org/abs/2110.11560
Autor:
Wang, Xu, Zhang, Hainan, Zhao, Shuai, Zou, Yanyan, Chen, Hongshen, Ding, Zhuoye, Cheng, Bo, Lan, Yanyan
Despite the success of neural dialogue systems in achieving high performance on the leader-board, they cannot meet users' requirements in practice, due to their poor reasoning skills. The underlying reason is that most neural dialogue models only cap
Externí odkaz:
http://arxiv.org/abs/2109.10510
Being able to reply with a related, fluent, and informative response is an indispensable requirement for building high-quality conversational agents. In order to generate better responses, some approaches have been proposed, such as feeding extra inf
Externí odkaz:
http://arxiv.org/abs/2109.06471
Autor:
Liu, Junpeng, Zou, Yanyan, Zhang, Hainan, Chen, Hongshen, Ding, Zhuoye, Yuan, Caixia, Wang, Xiaojie
Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In such a scenario, the topic of a conversation can vary upon progressio
Externí odkaz:
http://arxiv.org/abs/2109.04994
Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still hindered by the sparse learning signals. As a result, the sequential re
Externí odkaz:
http://arxiv.org/abs/2106.14031
Autor:
Zhan, Haolan, Zhang, Hainan, Chen, Hongshen, Shen, Lei, Ding, Zhuoye, Bao, Yongjun, Yan, Weipeng, Lan, Yanyan
In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to
Externí odkaz:
http://arxiv.org/abs/2103.01594