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Papers

Abstract Syntax Networks for Code Generation and Semantic Parsing

2017-04-25 · ACL 2017 7 · Maxim Rabinovich, Mitchell Stern, Dan Klein

Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.

📄 PDF Abstract BibTeX arXiv:1704.07535

Code (1)

xiye17/torchASN pytorch

Tasks

Code GenerationDecoderSemantic Parsing

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