Bi-Directional Joint Neural Networks for Intent Classification and Slot Filling

Soyeon Caren Han, Siqu Long, Huichun Li, Henry Weld, Josiah Poon

Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks proceeded independently. However, more recently joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. In this paper, we propose a bi-directional joint model for intent classification and slot filling, which includes a multi-stage hierarchical process via BERT and bi-directional joint natural language understanding mechanisms, including intent2slot and slot2intent, to obtain mutual performance enhancement between intent classification and slot filling. The evaluations show that our model achieves state-of-the-art results on intent classification accuracy, slot filling F1, and significantly improves sentence-level semantic frame accuracy when applied to publicly available benchmark datasets, ATIS (88.6%) and SNIPS (92.8%).

doi: 10.21437/Interspeech.2021-2044

Cite as: Han, S.C., Long, S., Li, H., Weld, H., Poon, J. (2021) Bi-Directional Joint Neural Networks for Intent Classification and Slot Filling. Proc. Interspeech 2021, 4743-4747, doi: 10.21437/Interspeech.2021-2044

@inproceedings{han21f_interspeech,
  author={Soyeon Caren Han and Siqu Long and Huichun Li and Henry Weld and Josiah Poon},
  title={{Bi-Directional Joint Neural Networks for Intent Classification and Slot Filling}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={4743--4747},
  doi={10.21437/Interspeech.2021-2044}
}