@inproceedings{cole-etal-2021-graph-based,
title = "Graph-Based Decoding for Task Oriented Semantic Parsing",
author = "Cole, Jeremy and
Jiang, Nanjiang and
Pasupat, Panupong and
He, Luheng and
Shaw, Peter",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.341",
doi = "10.18653/v1/2021.findings-emnlp.341",
pages = "4057--4065",
abstract = "The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In this work, we explore an alternative paradigm. We formulate semantic parsing as a dependency parsing task, applying graph-based decoding techniques developed for syntactic parsing. We compare various decoding techniques given the same pre-trained Transformer encoder on the TOP dataset, including settings where training data is limited or contains only partially-annotated examples. We find that our graph-based approach is competitive with sequence decoders on the standard setting, and offers significant improvements in data efficiency and settings where partially-annotated data is available.",
}
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<abstract>The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In this work, we explore an alternative paradigm. We formulate semantic parsing as a dependency parsing task, applying graph-based decoding techniques developed for syntactic parsing. We compare various decoding techniques given the same pre-trained Transformer encoder on the TOP dataset, including settings where training data is limited or contains only partially-annotated examples. We find that our graph-based approach is competitive with sequence decoders on the standard setting, and offers significant improvements in data efficiency and settings where partially-annotated data is available.</abstract>
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%0 Conference Proceedings
%T Graph-Based Decoding for Task Oriented Semantic Parsing
%A Cole, Jeremy
%A Jiang, Nanjiang
%A Pasupat, Panupong
%A He, Luheng
%A Shaw, Peter
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F cole-etal-2021-graph-based
%X The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In this work, we explore an alternative paradigm. We formulate semantic parsing as a dependency parsing task, applying graph-based decoding techniques developed for syntactic parsing. We compare various decoding techniques given the same pre-trained Transformer encoder on the TOP dataset, including settings where training data is limited or contains only partially-annotated examples. We find that our graph-based approach is competitive with sequence decoders on the standard setting, and offers significant improvements in data efficiency and settings where partially-annotated data is available.
%R 10.18653/v1/2021.findings-emnlp.341
%U https://aclanthology.org/2021.findings-emnlp.341
%U https://doi.org/10.18653/v1/2021.findings-emnlp.341
%P 4057-4065
Markdown (Informal)
[Graph-Based Decoding for Task Oriented Semantic Parsing](https://aclanthology.org/2021.findings-emnlp.341) (Cole et al., Findings 2021)
ACL
- Jeremy Cole, Nanjiang Jiang, Panupong Pasupat, Luheng He, and Peter Shaw. 2021. Graph-Based Decoding for Task Oriented Semantic Parsing. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4057–4065, Punta Cana, Dominican Republic. Association for Computational Linguistics.