Abstract
Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system. In this paper, we propose a novel non-autoregressive model named SlotRefine for joint intent detection and slot filling. Besides, we design a novel two-pass iteration mechanism to handle the uncoordinated slots problem caused by conditional independence of non-autoregressive model. Experiments demonstrate that our model significantly outperforms previous models in slot filling task, while considerably speeding up the decoding (up to x10.77). In-depth analysis show that 1) pretraining schemes could further enhance our model; 2) two-pass mechanism indeed remedy the uncoordinated slots.- Anthology ID:
- 2020.emnlp-main.152
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1932–1937
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.152
- DOI:
- 10.18653/v1/2020.emnlp-main.152
- Cite (ACL):
- Di Wu, Liang Ding, Fan Lu, and Jian Xie. 2020. SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1932–1937, Online. Association for Computational Linguistics.
- Cite (Informal):
- SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling (Wu et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.152.pdf
- Code
- moore3930/SlotRefine
- Data
- SNIPS