On the Challenges of Fully Incremental Neural Dependency Parsing
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On the Challenges of Fully Incremental Neural Dependency ParsingDate
2023-11Citation
Ana Ezquerro, Carlos Gómez-Rodríguez, and David Vilares. 2023. On the Challenges of Fully Incremental Neural Dependency Parsing. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 52–66, Nusa Dua, Bali. Association for Computational Linguistics.
Abstract
[Absctract]: 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 sequencelabeling 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.
Keywords
Incremental parsing
Neural encoders
Psycholinguistic Plausibility
Neural encoders
Psycholinguistic Plausibility
Description
Bali, Indonesia. November 1-4 2023.
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Atribución 3.0 España