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
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/N19-1055.pdf