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Many common sequential data sources, such as source code and naturallanguage, have a natural tree-structured representation. These trees can begenerated by fitting a sequence to a grammar, yielding a hierarchical orderingof the tokens in the sequence. This structure encodes a high degree ofsyntactic information, making it ideal for problems such as grammar correction.However, little work has been done to develop neural networks that can operateon and exploit tree-structured data. In this paper we present theTree-Transformer \textemdash{} a novel neural network architecture designed totranslate between arbitrary input and output trees. We applied thisarchitecture to correction tasks in both the source code and natural languagedomains. On source code, our model achieved an improvement of $25\%$$\text{F}0.5$ over the best sequential method. On natural language, we achievedcomparable results to the most complex state of the art systems, obtaining a$10\%$ improvement in recall on the CoNLL 2014 benchmark and the highest todate $\text{F}0.5$ score on the AESW benchmark of $50.43$.

Tree-Transformer: A Transformer-Based Method for Correction of Tree-Structured Data