On the Challenges of Fully Incremental Neural Dependency Parsing

Autor:	Ezquerro, Ana, Gómez-Rodríguez, Carlos, Vilares, David
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Since the popularization of BiLSTMs and Transformer-based bidirectional encoders, state-of-the-art syntactic parsers have lacked incrementality, requiring access to the whole sentence and deviating from human language processing. This paper explores whether fully incremental dependency parsing with modern architectures can be competitive. We build parsers combining strictly left-to-right neural encoders with fully incremental sequence-labeling and transition-based decoders. The results show that fully incremental parsing with modern architectures considerably lags behind bidirectional parsing, noting the challenges of psycholinguistically plausible parsing. Comment: Accepted at IJCNLP-AACL 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.16254 Zobrazit plný text záznamu View this record from Arxiv