Multitask Pointer Network for Multi-Representational Parsing

• URL: http://arxiv.org/abs/2009.09730v1
• Date: Mon, 21 Sep 2020 10:04:07 GMT
• Title: Multitask Pointer Network for Multi-Representational Parsing
• Authors: Daniel Fern\'andez-Gonz\'alez and Carlos G\'omez-Rodr\'iguez
• Abstract summary: We propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees. We develop a Pointer Network architecture with two separate task-specific decoders and a common encoder, and follow a learning strategy to jointly train them.
• Score: 0.34376560669160383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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