@inproceedings{kankanampati-etal-2020-multitask,
title = "Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations",
author = "Kankanampati, Yash and
Le Roux, Joseph and
Tomeh, Nadi and
Taji, Dima and
Habash, Nizar",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.225",
doi = "10.18653/v1/2020.coling-main.225",
pages = "2497--2508",
abstract = "In this paper we present a parsing model for projective dependency trees which takes advantage of the existence of complementary dependency annotations which is the case in Arabic, with the availability of CATiB and UD treebanks. Our system performs syntactic parsing according to both annotation types jointly as a sequence of arc-creating operations, and partially created trees for one annotation are also available to the other as features for the score function. This method gives error reduction of 9.9{\%} on CATiB and 6.1{\%} on UD compared to a strong baseline, and ablation tests show that the main contribution of this reduction is given by sharing tree representation between tasks, and not simply sharing BiLSTM layers as is often performed in NLP multitask systems.",
}
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<abstract>In this paper we present a parsing model for projective dependency trees which takes advantage of the existence of complementary dependency annotations which is the case in Arabic, with the availability of CATiB and UD treebanks. Our system performs syntactic parsing according to both annotation types jointly as a sequence of arc-creating operations, and partially created trees for one annotation are also available to the other as features for the score function. This method gives error reduction of 9.9% on CATiB and 6.1% on UD compared to a strong baseline, and ablation tests show that the main contribution of this reduction is given by sharing tree representation between tasks, and not simply sharing BiLSTM layers as is often performed in NLP multitask systems.</abstract>
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%0 Conference Proceedings
%T Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations
%A Kankanampati, Yash
%A Le Roux, Joseph
%A Tomeh, Nadi
%A Taji, Dima
%A Habash, Nizar
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F kankanampati-etal-2020-multitask
%X In this paper we present a parsing model for projective dependency trees which takes advantage of the existence of complementary dependency annotations which is the case in Arabic, with the availability of CATiB and UD treebanks. Our system performs syntactic parsing according to both annotation types jointly as a sequence of arc-creating operations, and partially created trees for one annotation are also available to the other as features for the score function. This method gives error reduction of 9.9% on CATiB and 6.1% on UD compared to a strong baseline, and ablation tests show that the main contribution of this reduction is given by sharing tree representation between tasks, and not simply sharing BiLSTM layers as is often performed in NLP multitask systems.
%R 10.18653/v1/2020.coling-main.225
%U https://aclanthology.org/2020.coling-main.225
%U https://doi.org/10.18653/v1/2020.coling-main.225
%P 2497-2508
Markdown (Informal)
[Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations](https://aclanthology.org/2020.coling-main.225) (Kankanampati et al., COLING 2020)
ACL