Left-to-Right Dependency Parsing with Pointer Networks
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Left-to-Right Dependency Parsing with Pointer NetworksData
2019Cita bibliográfica
Daniel Fernández-González and Carlos Gómez-Rodríguez. 2019. Left-to-Right Dependency Parsing with Pointer Networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 710–716, Minneapolis, Minnesota. Association for Computational Linguistics. doi: 10.18653/v1/N19-1076
Resumo
[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.
Palabras chave
Syntactics
Network frameworks
Right dependencies
Single models
Stack pointers
Topdown
Transition sequences
Transition system
Computational linguistics
Network frameworks
Right dependencies
Single models
Stack pointers
Topdown
Transition sequences
Transition system
Computational linguistics
Descrición
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
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Creative Commons Atribución 4.0 International.