@inproceedings{babu-etal-2021-non,
title = "Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog",
author = "Babu, Arun and
Shrivastava, Akshat and
Aghajanyan, Armen and
Aly, Ahmed and
Fan, Angela and
Ghazvininejad, Marjan",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.236/",
doi = "10.18653/v1/2021.naacl-main.236",
pages = "2969--2978",
abstract = "Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespread adoption of these models for real-time conversational use cases has been stymied by higher compute requirements and thus higher latency. In this work, we propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. By combining non-autoregressive prediction with convolutional neural networks, we achieve significant latency gains and parameter size reduction compared to traditional RNN models. Our novel architecture achieves up to an 81{\%} reduction in latency on TOP dataset and retains competitive performance to non-pretrained models on three different semantic parsing datasets."
}
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%0 Conference Proceedings
%T Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
%A Babu, Arun
%A Shrivastava, Akshat
%A Aghajanyan, Armen
%A Aly, Ahmed
%A Fan, Angela
%A Ghazvininejad, Marjan
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F babu-etal-2021-non
%X Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespread adoption of these models for real-time conversational use cases has been stymied by higher compute requirements and thus higher latency. In this work, we propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. By combining non-autoregressive prediction with convolutional neural networks, we achieve significant latency gains and parameter size reduction compared to traditional RNN models. Our novel architecture achieves up to an 81% reduction in latency on TOP dataset and retains competitive performance to non-pretrained models on three different semantic parsing datasets.
%R 10.18653/v1/2021.naacl-main.236
%U https://aclanthology.org/2021.naacl-main.236/
%U https://doi.org/10.18653/v1/2021.naacl-main.236
%P 2969-2978
Markdown (Informal)
[Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog](https://aclanthology.org/2021.naacl-main.236/) (Babu et al., NAACL 2021)
ACL
- Arun Babu, Akshat Shrivastava, Armen Aghajanyan, Ahmed Aly, Angela Fan, and Marjan Ghazvininejad. 2021. Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2969–2978, Online. Association for Computational Linguistics.