Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, Yun-Nung Chen
Abstract
Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively- Anthology ID:
- N18-2118
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 753–757
- Language:
- URL:
- https://aclanthology.org/N18-2118
- DOI:
- 10.18653/v1/N18-2118
- Cite (ACL):
- Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, and Yun-Nung Chen. 2018. Slot-Gated Modeling for Joint Slot Filling and Intent Prediction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 753–757, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Slot-Gated Modeling for Joint Slot Filling and Intent Prediction (Goo et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/N18-2118.pdf
- Code
- additional community code
- Data
- ATIS, SNIPS