Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations

Yash Kankanampati, Joseph Le Roux, Nadi Tomeh, Dima Taji, Nizar Habash


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.
Anthology ID:
2020.coling-main.225
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2497–2508
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.225
DOI:
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http://aclanthology.lst.uni-saarland.de/2020.coling-main.225.pdf