Hexatagging: Projective Dependency Parsing as Tagging
- URL: http://arxiv.org/abs/2306.05477v1
- Date: Thu, 8 Jun 2023 18:02:07 GMT
- Title: Hexatagging: Projective Dependency Parsing as Tagging
- Authors: Afra Amini, Tianyu Liu, Ryan Cotterell
- Abstract summary: We introduce a novel dependency, the hexatagger, that constructs dependency trees by tagging the words in a sentence with elements from a finite set of possible tags.
Our approach is fully parallelizable at training time, i.e., the structure-building actions needed to build a dependency parse can be predicted in parallel to each other.
We achieve state-of-the-art performance of 96.4 LAS and 97.4 UAS on the Penn Treebank test set.
- Score: 63.5392760743851
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a novel dependency parser, the hexatagger, that constructs
dependency trees by tagging the words in a sentence with elements from a finite
set of possible tags. In contrast to many approaches to dependency parsing, our
approach is fully parallelizable at training time, i.e., the structure-building
actions needed to build a dependency parse can be predicted in parallel to each
other. Additionally, exact decoding is linear in time and space complexity.
Furthermore, we derive a probabilistic dependency parser that predicts hexatags
using no more than a linear model with features from a pretrained language
model, i.e., we forsake a bespoke architecture explicitly designed for the
task. Despite the generality and simplicity of our approach, we achieve
state-of-the-art performance of 96.4 LAS and 97.4 UAS on the Penn Treebank test
set. Additionally, our parser's linear time complexity and parallelism
significantly improve computational efficiency, with a roughly 10-times
speed-up over previous state-of-the-art models during decoding.
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