Syntactic Graph Convolutional Network for Spoken Language Understanding
Keqing He, Shuyu Lei, Yushu Yang, Huixing Jiang, Zhongyuan Wang
Abstract
Slot filling and intent detection are two major tasks for spoken language understanding. In most existing work, these two tasks are built as joint models with multi-task learning with no consideration of prior linguistic knowledge. In this paper, we propose a novel joint model that applies a graph convolutional network over dependency trees to integrate the syntactic structure for learning slot filling and intent detection jointly. Experimental results show that our proposed model achieves state-of-the-art performance on two public benchmark datasets and outperforms existing work. At last, we apply the BERT model to further improve the performance on both slot filling and intent detection.- Anthology ID:
- 2020.coling-main.246
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2728–2738
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.246
- DOI:
- 10.18653/v1/2020.coling-main.246
- Cite (ACL):
- Keqing He, Shuyu Lei, Yushu Yang, Huixing Jiang, and Zhongyuan Wang. 2020. Syntactic Graph Convolutional Network for Spoken Language Understanding. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2728–2738, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Syntactic Graph Convolutional Network for Spoken Language Understanding (He et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.246.pdf
- Data
- ATIS, SNIPS