论文标题
递归非自动回调图形变压器，用于依赖性解析，并进行迭代精致
Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement
论文作者
论文摘要
我们通过递归应用非AutoreSpressive Graph to Graph Troph Transformer的递归应用，将递归的非自动向上图形变压器架构（RNGTR）提出，并将其应用于句法依赖性分析。我们使用用BERT预先训练的改进模型来证明RNGTR对几个依赖性语料库的功能和有效性。我们还介绍了句法变压器（Syntr），这是一种类似于我们的改进模型的非恢复解析器。 RNGTR可以提高来自通用依赖性Treebanks，英语和中文宾夕法尼亚州立Treebanks和德国Conll2009语料库的13种语言的各种初始解析器的准确性，甚至可以改善Syntr获得的新的最先进的结果，从而显着改善了所有公司测试的您的现状。
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.
