Fernández-González, DanielGómez-Rodríguez, Carlos2024-01-232024-01-232019Daniel 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/N19-1076http://hdl.handle.net/2183/35082Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)[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.engCreative Commons Atribución 4.0 International.http://creativecommons.org/licenses/by/3.0/es/SyntacticsNetwork frameworksRight dependenciesSingle modelsStack pointersTopdownTransition sequencesTransition systemComputational linguisticsconference outputopen access10.18653/v1/N19-1076