A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling

Yu Wang, Yilin Shen, Hongxia Jin


Abstract
Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on these tasks . The most effective algorithms are based on the structures of sequence to sequence models (or “encoder-decoder” models), and generate the intents and semantic tags either using separate models. Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. None of the approaches consider the cross-impact between the intent detection task and the slot filling task. In this paper, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a decoder achieves state-of-art result on the benchmark ATIS data, with about 0.5% intent accuracy improvement and 0.9 % slot filling improvement.
Anthology ID:
N18-2050
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:
309–314
Language:
URL:
https://aclanthology.org/N18-2050
DOI:
10.18653/v1/N18-2050
Bibkey:
Cite (ACL):
Yu Wang, Yilin Shen, and Hongxia Jin. 2018. A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling. 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 309–314, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (Wang et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-2050.pdf
Data
ATIS