Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog

Rashmi Gangadharaiah, Balakrishnan Narayanaswamy


Abstract
Neural network models have recently gained traction for sentence-level intent classification and token-based slot-label identification. In many real-world scenarios, users have multiple intents in the same utterance, and a token-level slot label can belong to more than one intent. We investigate an attention-based neural network model that performs multi-label classification for identifying multiple intents and produces labels for both intents and slot-labels at the token-level. We show state-of-the-art performance for both intent detection and slot-label identification by comparing against strong, recently proposed models. Our model provides a small but statistically significant improvement of 0.2% on the predominantly single-intent ATIS public data set, and 55% intent accuracy improvement on an internal multi-intent dataset.
Anthology ID:
N19-1055
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
564–569
Language:
URL:
https://aclanthology.org/N19-1055
DOI:
10.18653/v1/N19-1055
Bibkey:
Cite (ACL):
Rashmi Gangadharaiah and Balakrishnan Narayanaswamy. 2019. Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 564–569, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog (Gangadharaiah & Narayanaswamy, NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1055.pdf
Video:
 https://vimeo.com/353455476