papers AI Learner
The Github is limit! Click to go to the new site.

Left-to-Right Dependency Parsing with Pointer Networks

2019-03-20
Daniel Fernández-González, Carlos Gómez-Rodríguez

Abstract

We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building n attachments, with n being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2n-1 actions to n. This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04% UAS, 94.43% LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested.

Abstract (translated by Google)
URL

http://arxiv.org/abs/1903.08445

PDF

http://arxiv.org/pdf/1903.08445


Comments