Hexatagging：将投射依存句法分析作为标注任务
Hexatagging: Projective Dependency Parsing as Tagging
AI总结：
提出名为hexatagger的依存句法分析器，通过将单词标注为有限标签来构建依存树，实现训练时完全并行化和线性时空复杂度的精确解码，并在Penn Treebank上取得SOTA性能且解码速度提升约10倍。
摘要：
我们提出了一种新颖的依存句法分析器hexatagger，它通过为句子中的单词标注来自有限可能标签集合的元素来构建依存树。与许多依存句法分析方法不同，我们的方法在训练时完全可并行化，即构建依存分析树所需的构建结构动作可以相互并行地预测。此外，精确解码在时间和空间复杂度上均为线性。进一步地，我们推导出了一个概率依存句法分析器，它仅使用带有预训练语言模型特征的线性模型来预测hexatags，即我们放弃了为该任务显式设计的专门架构。尽管我们的方法具有通用性和简单性，但我们在Penn Treebank测试集上实现了96.4 LAS和97.4 UAS的最先进性能。此外，我们分析器的线性时间复杂度和并行性显著提高了计算效率，在解码过程中比以前的最先进模型快了约10倍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
