Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Mao, Xianling"'
Recently, the topic-grounded dialogue (TGD) system has become increasingly popular as its powerful capability to actively guide users to accomplish specific tasks through topic-guided conversations. Most existing works utilize side information (\eg t
Externí odkaz:
http://arxiv.org/abs/2406.01988
Temporal Knowledge Graph (TKG) is an extension of regular knowledge graph by attaching the time scope. Existing temporal knowledge graph question answering (TKGQA) models solely approach simple questions, owing to the prior assumption that each quest
Externí odkaz:
http://arxiv.org/abs/2401.02212
Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. Previous approaches have explored explicitly user profile modeling using text descriptions, implicit derivation of user embeddi
Externí odkaz:
http://arxiv.org/abs/2310.18342
Endowing chatbots with a consistent personality plays a vital role for agents to deliver human-like interactions. However, existing personalized approaches commonly generate responses in light of static predefined personas depicted with textual descr
Externí odkaz:
http://arxiv.org/abs/2208.10816
Autor:
Yusuf, Abdulganiyu Abdu, Feng, Chong, Mao, Xianling, Haruna, Yunusa, Li, Xinyan, Duma, Ramadhani Ally
Publikováno v:
In Neurocomputing 21 January 2025 614
Publikováno v:
Proceedings of the 36th AAAI Conference on Artificial Intelligence, 2022
Bundle recommendation aims to recommend the user a bundle of items as a whole. Nevertheless, they usually neglect the diversity of the user's intents on adopting items and fail to disentangle the user's intents in representations. In the real scenari
Externí odkaz:
http://arxiv.org/abs/2202.11425
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches n
Externí odkaz:
http://arxiv.org/abs/2106.03044
Most existing named entity recognition (NER) approaches are based on sequence labeling models, which focus on capturing the local context dependencies. However, the way of taking one sentence as input prevents the modeling of non-sequential global co
Externí odkaz:
http://arxiv.org/abs/2106.00887
Autor:
Wei, Wei, Liu, Jiayi, Mao, Xianling, Guo, Guibin, Zhu, Feida, Zhou, Pan, Hu, Yuchong, Feng, Shanshan
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches n
Externí odkaz:
http://arxiv.org/abs/2011.07432
Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks, e.g., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc. Though prevalent and effective in many downstream applications (e.g., in
Externí odkaz:
http://arxiv.org/abs/2011.06727