Weipeng Chen
2024
KEEP CHATTING! An Attractive Dataset for Continuous Conversation Agents
Yihe Wang
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Jin Liu
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Yao Wan
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Yitong Li
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Zifeng Liu
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Weipeng Chen
Findings of the Association for Computational Linguistics: ACL 2024
Ongoing chatting is an important step for conversational agents to build long-term connections with people. However, people tend to quickly lose interest in chatting if the conversational agent’s words are not engaging enough. In this paper, we present a novel task of increasing users’ willingness to continue talking to the agent.We collect a dataset named ContinuousChat by: (i) collecting personas and revising them, and then expanding the personas to detailed-personas through experiences, daily life, future plans, or interesting stories; (ii) expanding detailed-personas into the dialogues, and inject emotions and feelings into them; (iii) rewriting the dialogues in specific styles through few-shot prompt, conditioning on handwritten style-specific examples.We benchmark LLMs on ContinuousChat Dataset using both fine-tuning and in-context learning settings. Experiments over publicly available models demonstrate that although there is substantial room for improvement in generating style-specific dialogues, our ContinuousChat dataset is valuable in guiding conversational agents to generate more attractive dialogues and increase users’ willingness to continue the conversations.
2021
Multi-Lingual Question Generation with Language Agnostic Language Model
Bingning Wang
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Ting Yao
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Weipeng Chen
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Jingfang Xu
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Xiaochuan Wang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
2011
Coreference Resolution System using Maximum Entropy Classifier
Weipeng Chen
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Muyu Zhang
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Bing Qin
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task