@inproceedings{gangadharaiah-narayanaswamy-2019-joint,
title = "Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog",
author = "Gangadharaiah, Rashmi and
Narayanaswamy, Balakrishnan",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1055",
doi = "10.18653/v1/N19-1055",
pages = "564--569",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog
%A Gangadharaiah, Rashmi
%A Narayanaswamy, Balakrishnan
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F gangadharaiah-narayanaswamy-2019-joint
%X 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.
%R 10.18653/v1/N19-1055
%U https://aclanthology.org/N19-1055
%U https://doi.org/10.18653/v1/N19-1055
%P 564-569
Markdown (Informal)
[Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog](https://aclanthology.org/N19-1055) (Gangadharaiah & Narayanaswamy, NAACL 2019)
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.