@inproceedings{platanios-etal-2021-value,
title = "Value-Agnostic Conversational Semantic Parsing",
author = "Platanios, Emmanouil Antonios and
Pauls, Adam and
Roy, Subhro and
Zhang, Yuchen and
Kyte, Alexander and
Guo, Alan and
Thomson, Sam and
Krishnamurthy, Jayant and
Wolfe, Jason and
Andreas, Jacob and
Klein, Dan",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.284/",
doi = "10.18653/v1/2021.acl-long.284",
pages = "3666--3681",
abstract = "Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses. Existing parsers typically condition on rich representations of history that include the complete set of values and computations previously discussed. We propose a model that abstracts over values to focus prediction on type- and function-level context. This approach provides a compact encoding of dialogue histories and predicted programs, improving generalization and computational efficiency. Our model incorporates several other components, including an atomic span copy operation and structural enforcement of well-formedness constraints on predicted programs, that are particularly advantageous in the low-data regime. Trained on the SMCalFlow and TreeDST datasets, our model outperforms prior work by 7.3{\%} and 10.6{\%} respectively in terms of absolute accuracy. Trained on only a thousand examples from each dataset, it outperforms strong baselines by 12.4{\%} and 6.4{\%}. These results indicate that simple representations are key to effective generalization in conversational semantic parsing."
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<abstract>Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses. Existing parsers typically condition on rich representations of history that include the complete set of values and computations previously discussed. We propose a model that abstracts over values to focus prediction on type- and function-level context. This approach provides a compact encoding of dialogue histories and predicted programs, improving generalization and computational efficiency. Our model incorporates several other components, including an atomic span copy operation and structural enforcement of well-formedness constraints on predicted programs, that are particularly advantageous in the low-data regime. Trained on the SMCalFlow and TreeDST datasets, our model outperforms prior work by 7.3% and 10.6% respectively in terms of absolute accuracy. Trained on only a thousand examples from each dataset, it outperforms strong baselines by 12.4% and 6.4%. These results indicate that simple representations are key to effective generalization in conversational semantic parsing.</abstract>
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%0 Conference Proceedings
%T Value-Agnostic Conversational Semantic Parsing
%A Platanios, Emmanouil Antonios
%A Pauls, Adam
%A Roy, Subhro
%A Zhang, Yuchen
%A Kyte, Alexander
%A Guo, Alan
%A Thomson, Sam
%A Krishnamurthy, Jayant
%A Wolfe, Jason
%A Andreas, Jacob
%A Klein, Dan
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F platanios-etal-2021-value
%X Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses. Existing parsers typically condition on rich representations of history that include the complete set of values and computations previously discussed. We propose a model that abstracts over values to focus prediction on type- and function-level context. This approach provides a compact encoding of dialogue histories and predicted programs, improving generalization and computational efficiency. Our model incorporates several other components, including an atomic span copy operation and structural enforcement of well-formedness constraints on predicted programs, that are particularly advantageous in the low-data regime. Trained on the SMCalFlow and TreeDST datasets, our model outperforms prior work by 7.3% and 10.6% respectively in terms of absolute accuracy. Trained on only a thousand examples from each dataset, it outperforms strong baselines by 12.4% and 6.4%. These results indicate that simple representations are key to effective generalization in conversational semantic parsing.
%R 10.18653/v1/2021.acl-long.284
%U https://aclanthology.org/2021.acl-long.284/
%U https://doi.org/10.18653/v1/2021.acl-long.284
%P 3666-3681
Markdown (Informal)
[Value-Agnostic Conversational Semantic Parsing](https://aclanthology.org/2021.acl-long.284/) (Platanios et al., ACL-IJCNLP 2021)
ACL
- Emmanouil Antonios Platanios, Adam Pauls, Subhro Roy, Yuchen Zhang, Alexander Kyte, Alan Guo, Sam Thomson, Jayant Krishnamurthy, Jason Wolfe, Jacob Andreas, and Dan Klein. 2021. Value-Agnostic Conversational Semantic Parsing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3666–3681, Online. Association for Computational Linguistics.