While most neural generative models generate outputs in a single pass, the
human creative process is usually one of iterative building and refinement.
Recent work has proposed models of editing processes, but these mostly focus on
editing sequential data and/or only model a single editing pass. In this paper,
we present a generic model for incremental editing of structured data (i.e.,
"structural edits"). Particularly, we focus on tree-structured data, taking
abstract syntax trees of computer programs as our canonical example. Our editor
learns to iteratively generate tree edits (e.g., deleting or adding a subtree)
and applies them to the partially edited data, thereby the entire editing
process can be formulated as consecutive, incremental tree transformations. To
show the unique benefits of modeling tree edits directly, we further propose a
novel edit encoder for learning to represent edits, as well as an imitation
learning method that allows the editor to be more robust. We evaluate our
proposed editor on two source code edit datasets, where results show that, with
the proposed edit encoder, our editor significantly improves accuracy over
previous approaches that generate the edited program directly in one pass.
Finally, we demonstrate that training our editor to imitate experts and correct
its mistakes dynamically can further improve its performance.

提出一种适用于结构化数据的增量编辑模型，以树状结构数据为例，采用编辑编码器和模仿学习方法，实现树的连续渐进式变换，与采用单次直接生成的编辑方法相比，明显提高精度和健壮性。

通过增量树转换学习结构编辑

Learning Structural Edits via Incremental Tree Transformations

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

本文提出 Tree-Transformer 神经网络架构，可用于树状结构数据的矫正任务，并在源代码和自然语言两个领域中分别取得了 25% 和 10% 的改进。