---
title: "Cross-Domain Slot Filling as Machine Reading Comprehension"
authors:
  - "Mengshi Yu"
  - "Jian Liu"
  - "Yufeng Chen"
  - "Jinan Xu"
  - "Yujie Zhang"
doi: "10.24963/ijcai.2021/550"
url: "https://ijcai.org/proceedings/2021/550"
pdf: "https://ijcai.org/proceedings/2021/0550.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "3992-3998"
track: "Main Track"
keywords:
  - "Natural Language Processing: Dialogue"
  - "Natural Language Processing: Information Extraction"
---

# Cross-Domain Slot Filling as Machine Reading Comprehension

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

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 3992-3998. DOI: [10.24963/ijcai.2021/550](https://doi.org/10.24963/ijcai.2021/550)

## 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.