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• W3087549714 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." @default.
• W3087549714 created "2020-09-25" @default.
• W3087549714 creator A5019810499 @default.
• W3087549714 creator A5030874155 @default.
• W3087549714 date "2020-09-21" @default.
• W3087549714 modified "2023-09-27" @default.
• W3087549714 title "Multitask Pointer Network for Multi-Representational Parsing" @default.
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