Zhufeng Pan


2021

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Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text
Kunwoo Park | Zhufeng Pan | Jungseock Joo
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2019

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Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration
Zhufeng Pan | Kun Bai | Yan Wang | Lianqiang Zhou | Xiaojiang Liu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In multi-turn dialogue, utterances do not always take the full form of sentences. These incomplete utterances will greatly reduce the performance of open-domain dialogue systems. Restoring more incomplete utterances from context could potentially help the systems generate more relevant responses. To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context. We also propose a “pick-and-combine” model to restore the incomplete utterance from its context. Experimental results demonstrate that the annotated dataset and the proposed approach significantly boost the response quality of both single-turn and multi-turn dialogue systems.