Papers › Left-to-Right Dependency Parsing with Pointer Networks

Left-to-Right Dependency Parsing with Pointer Networks

20 Mar 2019arXiv:1903.08445archive 2025-07-28

Daniel Fernández-González, Carlos Gómez-Rodríguez

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.

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Code

danifg/Left2Right-Pointer-Parser officialmentioned in papermentioned on GitHubpytorch report
danifg/SyntacticPointer mentioned on GitHubpytorchGPL-3.0 report

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Tasks

Dependency ParsingSentence

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Dependency Parsing Penn Treebank Left-to-Right Pointer Network LAS 94.43 #11 of 22 Archive leaderboard report
Dependency Parsing Penn Treebank Left-to-Right Pointer Network UAS 96.04 #11 of 22 Archive leaderboard report

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Methods

LSTMPointer NetworkSigmoid ActivationSoftmaxTanh Activation

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