We propose the Graph2Graph Transformer architecture for conditioning on and
predicting arbitrary graphs, and apply it to the challenging task of
transition-based dependency parsing. After proposing two novel Transformer
models of transition-based dependency parsing as strong baselines, we show that
adding the proposed mechanisms for conditioning on and predicting graphs of
Graph2Graph Transformer results in significant improvements, both with and
without BERT pre-training. The novel baselines and their integration with
Graph2Graph Transformer significantly outperform the state-of-the-art in
traditional transition-based dependency parsing on both English Penn Treebank,
and 13 languages of Universal Dependencies Treebanks. Graph2Graph Transformer
can be integrated with many previous structured prediction methods, making it
easy to apply to a wide range of NLP tasks.

我们提出了 Graph2Graph Transformer 架构，该架构用于对任意图进行条件和预测，并将其应用于具有挑战性的转换依赖分析任务中，这种新型基线结构可以集成许多以前的结构化预测方法，能够轻松应用于各种自然语言处理任务，同时在传统转换依赖分析中表现出较高的准确性。