@inproceedings{dozat-manning-2018-simpler,
title = "Simpler but More Accurate Semantic Dependency Parsing",
author = "Dozat, Timothy and
Manning, Christopher D.",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2077",
doi = "10.18653/v1/P18-2077",
pages = "484--490",
abstract = "While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves state-of-the-art performance, beating the previous, substantially more complex state-of-the-art system by 0.6{\%} labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 1.9{\%} labeled F1.",
}
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%0 Conference Proceedings
%T Simpler but More Accurate Semantic Dependency Parsing
%A Dozat, Timothy
%A Manning, Christopher D.
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F dozat-manning-2018-simpler
%X While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves state-of-the-art performance, beating the previous, substantially more complex state-of-the-art system by 0.6% labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.
%R 10.18653/v1/P18-2077
%U https://aclanthology.org/P18-2077
%U https://doi.org/10.18653/v1/P18-2077
%P 484-490
Markdown (Informal)
[Simpler but More Accurate Semantic Dependency Parsing](https://aclanthology.org/P18-2077) (Dozat & Manning, ACL 2018)
ACL
- Timothy Dozat and Christopher D. Manning. 2018. Simpler but More Accurate Semantic Dependency Parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 484–490, Melbourne, Australia. Association for Computational Linguistics.