A Bert Based Joint Learning Model with Feature Gated Mechanism for Spoken Language Understanding

Wang Zhang; Lei Jiang; Shaokang Zhang; Shuo Wang; Jianlong Tan

ICASSP 2022accepted0 citations

A Bert Based Joint Learning Model with Feature Gated Mechanism for Spoken Language Understanding

Wang Zhang, Lei Jiang, Shaokang Zhang, Shuo Wang, Jianlong Tan

Abstract

Intent detection (ID) and slot filling (SF) are two major tasks for spoken language understanding (SLU). Recent joint learning approaches consider the relationship between intent detection and slot filling, which leverage the shared knowledge across two tasks to benefit each other. However, most existing methods do not make full use of the BERT model and gate mechanisms to improve the semantic correlation between slot filling and intent detection tasks. In this paper, we propose a joint learning model based on BERT, which introduce dual encoder structure and utilizes semantic information by performing feature gate mechanisms in predicting intents and slots. Experimental results demonstrate that our proposed method provides very competitive results on CAIS and DDoST datasets.

BibTeX

@inproceedings{icassp2022_abertbasedjointl,
  title = {A Bert Based Joint Learning Model with Feature Gated Mechanism for Spoken Language Understanding},
  author = {Wang Zhang and Lei Jiang and Shaokang Zhang and Shuo Wang and Jianlong Tan},
  booktitle = {ICASSP 2022},
  year = {2022}
}