Learning Structural Edits via Incremental Tree Transformations

Understand

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.