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Sequence Tagging for Fast Dependency Parsing

CITIC, FASTPARSE Lab, Departamento de Computación, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
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Presented at XoveTIC Congress, A Coruña, Spain, 5–6 September 2019.
Proceedings 2019, 21(1), 49; https://doi.org/10.3390/proceedings2019021049
Published: 26 August 2019
(This article belongs to the Proceedings of The 2nd XoveTIC Conference (XoveTIC 2019))
Dependency parsing has been built upon the idea of using parsing methods based on shift-reduce or graph-based algorithms in order to identify binary dependency relations between the words in a sentence. In this study we adopt a radically different approach and cast full dependency parsing as a pure sequence tagging task. In particular, we apply a linearization function to the tree that results in an output label for each token that conveys information about the word’s dependency relations. We then follow a supervised strategy and train a bidirectional long short-term memory network to learn to predict such linearized trees. Contrary to the previous studies attempting this, the results show that this approach not only leads to accurate but also fast dependency parsing. Furthermore, we obtain even faster and more accurate parsers by recasting the problem as multitask learning, with a twofold objective: to reduce the output vocabulary and also to exploit hidden patterns coming from a second parsing paradigm (constituent grammars) when used as an auxiliary task.
Keywords: Natural Language Processing; Syntax; Parsing; Sequence Tagging, Multitask Learning Natural Language Processing; Syntax; Parsing; Sequence Tagging, Multitask Learning
MDPI and ACS Style

Strzyz, M.; Vilares, D.; Gómez-Rodríguez, C. Sequence Tagging for Fast Dependency Parsing. Proceedings 2019, 21, 49.

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