@article{FerGomNeuComp2023,
title = {Discontinuous grammar as a foreign language},
journal = {Neurocomputing},
volume = {524},
pages = {43-58},
year = {2023},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2022.12.045},
url = {https://www.sciencedirect.com/science/article/pii/S092523122201551X},
author = {Daniel Fern\'andez-Gonz\'alez and Carlos G\'omez-Rodr\'iguez},
keywords = {Natural language processing, Computational linguistics, Parsing, Discontinuous constituent parsing, Neural network, Deep learning, Sequence-to-sequence model},
abstract = {In order to achieve deep natural language understanding, syntactic constituent parsing is a vital step, highly demanded by many artificial intelligence systems to process both text and speech. One of the most recent proposals is the use of standard sequence-to-sequence models to perform constituent parsing as a machine translation task, instead of applying task-specific parsers. While they show a competitive performance, these text-to-parse transducers are still lagging behind classic techniques in terms of accuracy, coverage and speed. To close the gap, we here extend the framework of sequence-to-sequence models for constituent parsing, not only by providing a more powerful neural architecture for improving their performance, but also by enlarging their coverage to handle the most complex syntactic phenomena: discontinuous structures. To that end, we design several novel linearizations that can fully produce discontinuities and, for the first time, we test a sequence-to-sequence model on the main discontinuous benchmarks, obtaining competitive results on par with task-specific discontinuous constituent parsers and achieving state-of-the-art scores on the (discontinuous) English Penn Treebank.}
}