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4 months ago

Left-to-Right Dependency Parsing with Pointer Networks

Daniel Fernández-González; Carlos Gómez-Rodríguez

Left-to-Right Dependency Parsing with Pointer Networks

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 2$n$-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.

Code Repositories

danifg/SyntacticPointer
pytorch
Mentioned in GitHub
danifg/Left2Right-Pointer-Parser
Official
pytorch
Mentioned in GitHub

Benchmarks

BenchmarkMethodologyMetrics
dependency-parsing-on-penn-treebankLeft-to-Right Pointer Network
LAS: 94.43
UAS: 96.04

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Left-to-Right Dependency Parsing with Pointer Networks | Papers | HyperAI