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.

本文介绍了一种新的依赖性分析器：Hexatagger，通过标记句子中的单词，并使用可能标记的有限集合来构建依赖关系树。我们的方法是完全可并行化的，并获得了 96.4 LAS 和 97.4 UAS 的最新性能。同时，我们的解析器的线性时间复杂度和并行性显着提高了计算效率，比先前的最先进模型快了大约 10 倍。