UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues
Xinyan Zhao | Bin He | Yasheng Wang | Yitong Li | Fei Mi | Yajiao Liu | Xin Jiang | Qun Liu | Huanhuan Chen
Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering
With the advances in deep learning, tremendous progress has been made with chit-chat dialogue systems and task-oriented dialogue systems. However, these two systems are often tackled separately in current methods. To achieve more natural interaction with humans, dialogue systems need to be capable of both chatting and accomplishing tasks. To this end, we propose a unified dialogue system (UniDS) with the two aforementioned skills. In particular, we design a unified dialogue data schema, compatible for both chit-chat and task-oriented dialogues. Besides, we propose a two-stage training method to train UniDS based on the unified dialogue data schema. UniDS does not need to adding extra parameters to existing chit-chat dialogue systems. Experimental results demonstrate that the proposed UniDS works comparably well as the state-of-the-art chit-chat dialogue systems and task-oriented dialogue systems. More importantly, UniDS achieves better robustness than pure dialogue systems and satisfactory switch ability between two types of dialogues.
Relation Classification with Entity Type Restriction
Shengfei Lyu | Huanhuan Chen
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Improve Chinese Word Embeddings by Exploiting Internal Structure
Jian Xu | Jiawei Liu | Liangang Zhang | Zhengyu Li | Huanhuan Chen
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Shengfei Lyu 1
- Jian Xu 1
- Jiawei Liu 1
- Liangang Zhang 1
- Zhengyu Li 1
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