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arXiv · 2009.09730

Multitask Pointer Network for Multi-Representational Parsing

Daniel Fernández-González·Carlos Gómez-Rodríguez
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Abstract

We propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees, supporting both continuous/projective and discontinuous/non-projective syntactic structures. To that end, we develop a Pointer Network architecture with two separate task-specific decoders and a common encoder, and follow a multitask learning strategy to jointly train them. The resulting quadratic system, not only becomes the first parser that can jointly produce both unrestricted constituent and dependency trees from a single model, but also proves that both syntactic formalisms can benefit from each other during training, achieving state-of-the-art accuracies in several widely-used benchmarks such as the continuous English and Chinese Penn Treebanks, as well as the discontinuous German NEGRA and TIGER datasets.

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Daniel Fernández-González, Carlos Gómez-Rodríguez. 2020-09-21. Multitask Pointer Network for Multi-Representational Parsing. https://doi.org/10.1016/j.knosys.2021.107760

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