%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