@inproceedings{wu-etal-2020-slotrefine,
title = "{S}lot{R}efine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling",
author = "Wu, Di and
Ding, Liang and
Lu, Fan and
Xie, Jian",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.152",
doi = "10.18653/v1/2020.emnlp-main.152",
pages = "1932--1937",
abstract = "Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system. In this paper, we propose a novel non-autoregressive model named SlotRefine for joint intent detection and slot filling. Besides, we design a novel two-pass iteration mechanism to handle the uncoordinated slots problem caused by conditional independence of non-autoregressive model. Experiments demonstrate that our model significantly outperforms previous models in slot filling task, while considerably speeding up the decoding (up to x10.77). In-depth analysis show that 1) pretraining schemes could further enhance our model; 2) two-pass mechanism indeed remedy the uncoordinated slots.",
}
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<abstract>Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system. In this paper, we propose a novel non-autoregressive model named SlotRefine for joint intent detection and slot filling. Besides, we design a novel two-pass iteration mechanism to handle the uncoordinated slots problem caused by conditional independence of non-autoregressive model. Experiments demonstrate that our model significantly outperforms previous models in slot filling task, while considerably speeding up the decoding (up to x10.77). In-depth analysis show that 1) pretraining schemes could further enhance our model; 2) two-pass mechanism indeed remedy the uncoordinated slots.</abstract>
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%0 Conference Proceedings
%T SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling
%A Wu, Di
%A Ding, Liang
%A Lu, Fan
%A Xie, Jian
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wu-etal-2020-slotrefine
%X Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system. In this paper, we propose a novel non-autoregressive model named SlotRefine for joint intent detection and slot filling. Besides, we design a novel two-pass iteration mechanism to handle the uncoordinated slots problem caused by conditional independence of non-autoregressive model. Experiments demonstrate that our model significantly outperforms previous models in slot filling task, while considerably speeding up the decoding (up to x10.77). In-depth analysis show that 1) pretraining schemes could further enhance our model; 2) two-pass mechanism indeed remedy the uncoordinated slots.
%R 10.18653/v1/2020.emnlp-main.152
%U https://aclanthology.org/2020.emnlp-main.152
%U https://doi.org/10.18653/v1/2020.emnlp-main.152
%P 1932-1937
Markdown (Informal)
[SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling](https://aclanthology.org/2020.emnlp-main.152) (Wu et al., EMNLP 2020)
ACL