We propose the Recursive Non-autoregressive Graph-to-Graph Transformer
architecture (RNGTr) for the iterative refinement of arbitrary graphs through
the recursive application of a non-autoregressive Graph-to-Graph Transformer
and apply it to syntactic dependency parsing. We demonstrate the power and
effectiveness of RNGTr on several dependency corpora, using a refinement model
pre-trained with BERT. We also introduce Syntactic Transformer (SynTr), a
non-recursive parser similar to our refinement model. RNGTr can improve the
accuracy of a variety of initial parsers on 13 languages from the Universal
Dependencies Treebanks, English and Chinese Penn Treebanks, and the German
CoNLL2009 corpus, even improving over the new state-of-the-art results achieved
by SynTr, significantly improving the state-of-the-art for all corpora tested.

本文提出了一种基于递归非自回归图到图变换器 (RNGTr) 的架构，用于任意图的迭代细化，通过应用非自回归图到图变换器的递归应用，并将其应用于句法依赖解析上。我们使用使用 BERT 预训练的细化模型展示了 RNGTr 的强大和有效性。我们还介绍了一款类似于我们优化模型的非递归解析器 Syntactic Transformer (SynTr)。RNGTr 可以提高 13 种语言的各种初始解析器的准确性，包括 Universal Dependencies Treebanks，英语和中文 Penn Treebanks，以及德语 CoNLL2009 语料库，并且在所有测试数据上显著超越了最新的最优结果的表现。

递归非自回归图到图转换器用于具备迭代优化的依存句法分析

Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency  Parsing with Iterative Refinement

We describe a baseline dependency parsing system for the CoNLL2017 Shared
Task. This system, which we call "ParseySaurus," uses the DRAGNN framework
[Kong et al, 2017] to combine transition-based recurrent parsing and tagging
with character-based word representations. On the v1.3 Universal Dependencies
Treebanks, the new system outpeforms the publicly available, state-of-the-art
"Parsey's Cousins" models by 3.47% absolute Labeled Accuracy Score (LAS) across
52 treebanks.

本文介绍了一种使用 DRAGNN 框架的依赖分析系统，结合基于转移的递归解析和标记，以及基于字符的单词表示，其在 v1.3 版本的通用依赖树库上，相较于现有的最先进的 “Parsey's Cousins” 模型，标注准确性得分提高了 3.47%。