@article{FerGomInfFus2023,
title = {Dependency parsing with bottom-up Hierarchical Pointer Networks},
journal = {Information Fusion},
volume = {91},
pages = {494-503},
year = {2023},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2022.10.023},
url = {https://www.sciencedirect.com/science/article/pii/S1566253522001993},
author = {Daniel Fern\'andez-Gonz\'alez and Carlos G\'omez-Rodr\'iguez},
keywords = {Natural language processing, Computational linguistics, Parsing, Dependency parsing, Neural network, Deep learning},
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.}
}