Proceedings

Cross-Domain Slot Filling as Machine Reading Comprehension

Mengshi Yu, Jian Liu, Yufeng Chen, Jinan Xu, Yujie Zhang

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3992-3998.

https://doi.org/10.24963/ijcai.2021/550

Keywords

  • Natural Language Processing: Dialogue
  • Natural Language Processing: Information Extraction

Abstract

With task-oriented dialogue systems being widely applied in everyday life, slot filling, the essential component of task-oriented dialogue systems, is required to be quickly adapted to new domains that contain domain-specific slots with few or no training data. Previous methods for slot filling usually adopt sequence labeling framework, which, however, often has limited ability when dealing with the domain-specific slots. In this paper, we take a new perspective on cross-domain slot filling by framing it as a machine reading comprehension (MRC) problem. Our approach firstly transforms slot names into well-designed queries, which contain rich informative prior knowledge and are very helpful for the detection of domain-specific slots. In addition, we utilize the large-scale MRC dataset for pre-training, which further alleviates the data scarcity problem. Experimental results on SNIPS and ATIS datasets show that our approach consistently outperforms the existing state-of-the-art methods by a large margin.

Mengshi Yu, Jian Liu, Yufeng Chen, Jinan Xu, and Yujie Zhang. 2021. Cross-Domain Slot Filling as Machine Reading Comprehension. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3992–3998. https://doi.org/10.24963/ijcai.2021/550
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