Neural Discontinuous Constituency Parsing

EMNLP 2017 Milo{\v{s}} Stanojevi{\'c}Raquel G. Alhama

One of the most pressing issues in discontinuous constituency transition-based parsing is that the relevant information for parsing decisions could be located in any part of the stack or the buffer. In this paper, we propose a solution to this problem by replacing the structured perceptron model with a recursive neural model that computes a global representation of the configuration, therefore allowing even the most remote parts of the configuration to influence the parsing decisions... (read more)

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