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pro vyhledávání: '"Lee, Jing Yang"'
Information-Seeking Dialogue (ISD) agents aim to provide accurate responses to user queries. While proficient in directly addressing user queries, these agents, as well as LLMs in general, predominantly exhibit reactive behavior, lacking the ability
Externí odkaz:
http://arxiv.org/abs/2410.15297
To engage human users in meaningful conversation, open-domain dialogue agents are required to generate diverse and contextually coherent dialogue. Despite recent advancements, which can be attributed to the usage of pretrained language models, the ge
Externí odkaz:
http://arxiv.org/abs/2311.10945
Despite recent progress in generative open-domain dialogue, the issue of low response diversity persists. Prior works have addressed this issue via either novel objective functions, alternative learning approaches such as variational frameworks, or a
Externí odkaz:
http://arxiv.org/abs/2311.10943
Autor:
Hoi Pong Nicholas Wong, Lee Jing Yang, Vikneshwaren S/O Senthamil Selvan, Jamie Yong Qi Lim, Wei Zheng So, Vineet Gauhar, Ho Yee Tiong
Publikováno v:
Société Internationale d’Urologie Journal, Vol 5, Iss 3, Pp 182-191 (2024)
TikTok has become a hub for easily accessible medical information. However, the quality and completeness of this information for testicular cancer has not been examined. Our study aims to assess the quality and completeness of testicular cancer infor
Externí odkaz:
https://doaj.org/article/e46aa2b7c9cc40e8a20913d55b765403
In recent years, latent variable models, such as the Conditional Variational Auto Encoder (CVAE), have been applied to both personalized and empathetic dialogue generation. Prior work have largely focused on generating diverse dialogue responses that
Externí odkaz:
http://arxiv.org/abs/2202.05971
The generation of personalized dialogue is vital to natural and human-like conversation. Typically, personalized dialogue generation models involve conditioning the generated response on the dialogue history and a representation of the persona/person
Externí odkaz:
http://arxiv.org/abs/2111.11363
Publikováno v:
Proceedings of the 25th Workshop on the Semantics and Pragmatics of Dialogue - Full Papers, pp 88-97 (2021)
Conventional approaches to personalized dialogue generation typically require a large corpus, as well as predefined persona information. However, in a real-world setting, neither a large corpus of training data nor persona information are readily ava
Externí odkaz:
http://arxiv.org/abs/2108.03377
Akademický článek
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Publikováno v:
Proceedings of the 4th Workshop on NLP for Conversational AI.
A major issue in open-domain dialogue generation is the agent’s tendency to generate repetitive and generic responses. The lack in response diversity has been addressed in recent years via the use of latent variable models, such as the Conditional
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::87c3449d70ec8b150f562ad8fb9b9b14
https://doi.org/10.18653/v1/2022.nlp4convai-1.1
https://doi.org/10.18653/v1/2022.nlp4convai-1.1
Akademický článek
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