Technical notes: Syntax-aware Representation Learning With Pointer Networks

03/17/2019

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by   Matteo Grella, et al.

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This is a work-in-progress report, which aims to share preliminary results of a novel sequence-to-sequence schema for dependency parsing that relies on a combination of a BiLSTM and two Pointer Networks (Vinyals et al., 2015), in which the final softmax function has been replaced with the logistic regression. The two pointer networks co-operate to develop a latent syntactic knowledge, by learning the lexical properties of "selection" and the lexical properties of "selectability", respectively. At the moment and without fine-tuning, the parser implementation gets a UAS of 93.14 Penn-treebank (Marcus et al., 1993) annotated with Stanford Dependencies: 2-3 under the SOTA but yet attractive as a baseline of the approach.