Accelerating Multiple Intent Detection and Slot Filling via Targeted Knowledge Distillation

Xuxin Cheng, Zhihong Zhu, Wanshi Xu, Yaowei Li, Hongxiang Li, Yuexian Zou


Abstract
Recent non-autoregressive Spoken Language Understanding (SLU) models have attracted increasing attention because of their encouraging inference speed. However, most of existing methods (1) suffer from the multi-modality problem since they have little prior knowledge about the reference during inference; (2) fail to achieve a satisfactory inference speed limited by their complex frameworks. To tackle these issues, in this paper, we propose a Targeted Knowledge Distillation Framework (TKDF) for multi-intent SLU, which utilizes the knowledge distillation method to improve the performance. Specifically, we first train an SLU model as the teacher model, which has higher accuracy while slower inference speed. Then we introduce an evaluator and apply a curriculum learning strategy to select proper targets for the student model. Experiment results on two public multi-intent datasets show that our approach can realize a flexible trade-off between inference speed and accuracy, achieving comparable performance to the state-of-the-art models while speeding up by over 4.5 times. More encouragingly, further analysis shows that distilling only 4% of the original data can help the student model outperform its counterpart trained on the original data by about 14.6% in terms of overall accuracy on MixATIS dataset.
Anthology ID:
2023.findings-emnlp.597
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8900–8910
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.597
DOI:
10.18653/v1/2023.findings-emnlp.597
Bibkey:
Cite (ACL):
Xuxin Cheng, Zhihong Zhu, Wanshi Xu, Yaowei Li, Hongxiang Li, and Yuexian Zou. 2023. Accelerating Multiple Intent Detection and Slot Filling via Targeted Knowledge Distillation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8900–8910, Singapore. Association for Computational Linguistics.
Cite (Informal):
Accelerating Multiple Intent Detection and Slot Filling via Targeted Knowledge Distillation (Cheng et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-emnlp.597.pdf