Dependency parsing with bottom-up Hierarchical Pointer Networks
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Dependency parsing with bottom-up Hierarchical Pointer NetworksDate
2023-03Citation
Fernández-González, Daniel; Gómez-Rodríguez, Carlos (2023): Dependency parsing with bottom-up Hierarchical Pointer Networks. Information Fusion 91: 494-503
Abstract
[Abstract] Dependency parsing is a crucial step towards deep language understanding and, therefore, widely demanded by numerous Natural Language Processing applications. In particular, left-to-right and top-down transition-based algorithms that rely on Pointer Networks are among the most accurate approaches for performing dependency parsing. Additionally, it has been observed for the top-down algorithm that Pointer Networks’ sequential decoding can be improved by implementing a hierarchical variant, more adequate to model dependency structures. Considering all this, we develop a bottom-up oriented Hierarchical Pointer Network for the left-to-right parser and propose two novel transition-based alternatives: an approach that parses a sentence in right-to-left order and a variant that does so from the outside in. We empirically test the proposed neural architecture with the different algorithms on a wide variety of languages, outperforming the original approach in practically all of them and setting new state-of-the-art results on the English and Chinese Penn Treebanks for non-contextualized and BERT-based embeddings.
Keywords
Natural language processing
Computational linguistics
Parsing
Dependency parsing
Neural networks
Deep learning
Computational linguistics
Parsing
Dependency parsing
Neural networks
Deep learning
Description
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG
Editor version
Rights
Atribución-NoComercial-SinDerivadas 4.o Internacional
ISSN
1872-6305