On the Challenges of Fully Incremental Neural Dependency Parsing
Ana Ezquerro Carlos Gómez-Rodríguez David Vilares
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.