@inproceedings{fernandez-gonzalez-gomez-rodriguez-2019-left,
title = "Left-to-Right Dependency Parsing with Pointer Networks",
author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel and
G{\'o}mez-Rodr{\'\i}guez, Carlos",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1076",
doi = "10.18653/v1/N19-1076",
pages = "710--716",
abstract = "We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building n attachments, with n being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2n-1 actions to n. This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04{\%} UAS, 94.43{\%} LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested.",
}
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%0 Conference Proceedings
%T Left-to-Right Dependency Parsing with Pointer Networks
%A Fernández-González, Daniel
%A Gómez-Rodríguez, Carlos
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F fernandez-gonzalez-gomez-rodriguez-2019-left
%X We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building n attachments, with n being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2n-1 actions to n. This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04% UAS, 94.43% LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested.
%R 10.18653/v1/N19-1076
%U https://aclanthology.org/N19-1076
%U https://doi.org/10.18653/v1/N19-1076
%P 710-716
Markdown (Informal)
[Left-to-Right Dependency Parsing with Pointer Networks](https://aclanthology.org/N19-1076) (Fernández-González & Gómez-Rodríguez, NAACL 2019)
ACL
- Daniel Fernández-González and Carlos Gómez-Rodríguez. 2019. Left-to-Right Dependency Parsing with Pointer Networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 710–716, Minneapolis, Minnesota. Association for Computational Linguistics.