@inproceedings{peng-etal-2017-deep,
title = "Deep Multitask Learning for Semantic Dependency Parsing",
author = "Peng, Hao and
Thomson, Sam and
Smith, Noah A.",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1186",
doi = "10.18653/v1/P17-1186",
pages = "2037--2048",
abstract = "We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms. By using efficient, nearly arc-factored inference and a bidirectional-LSTM composed with a multi-layer perceptron, our base system is able to significantly improve the state of the art for semantic dependency parsing, without using hand-engineered features or syntax. We then explore two multitask learning approaches{---}one that shares parameters across formalisms, and one that uses higher-order structures to predict the graphs jointly. We find that both approaches improve performance across formalisms on average, achieving a new state of the art. Our code is open-source and available at \url{https://github.com/Noahs-ARK/NeurboParser}.",
}
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%0 Conference Proceedings
%T Deep Multitask Learning for Semantic Dependency Parsing
%A Peng, Hao
%A Thomson, Sam
%A Smith, Noah A.
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F peng-etal-2017-deep
%X We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms. By using efficient, nearly arc-factored inference and a bidirectional-LSTM composed with a multi-layer perceptron, our base system is able to significantly improve the state of the art for semantic dependency parsing, without using hand-engineered features or syntax. We then explore two multitask learning approaches—one that shares parameters across formalisms, and one that uses higher-order structures to predict the graphs jointly. We find that both approaches improve performance across formalisms on average, achieving a new state of the art. Our code is open-source and available at https://github.com/Noahs-ARK/NeurboParser.
%R 10.18653/v1/P17-1186
%U https://aclanthology.org/P17-1186
%U https://doi.org/10.18653/v1/P17-1186
%P 2037-2048
Markdown (Informal)
[Deep Multitask Learning for Semantic Dependency Parsing](https://aclanthology.org/P17-1186) (Peng et al., ACL 2017)
ACL
- Hao Peng, Sam Thomson, and Noah A. Smith. 2017. Deep Multitask Learning for Semantic Dependency Parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2037–2048, Vancouver, Canada. Association for Computational Linguistics.