xiye17 / torchASN

A pytorch implementation of Abstract Syntax Networks

torchASN

A pytorch implementation of "Abstract Syntax Networks for Code Generation and Semantic Parsing".

Prerequisites

• pytorch >= 1.4.0

Run on Demo Dataset

Instructions of running torchASN on DeepRegex, an NL-to-Regex dataset, are included. This implementation achieves 61.6% DFA-accuracy on DeepRegex. For comparison, the performance of a seq-to-seq model with attention is 58.2%.

To run the code,

# Preprocess dataset
python dataset/turk/make_dataset.py

# Train a model. A pretrained model is included at checkpoints/turk/pretrained.bin.
./scripts/turk/train.sh

# Test a model. <model_file> is the pointer to the model, e.g., the pretrained one mentioned above.
./scripts/turk/test.sh <model_file>

Addapting to New Programming Language

Adapting to a new programming langauge requires following steps:

1. Write down the domain specific language in the Abstract Syntax Description Language (ASDL) form. (refer to data/turk/turk_asdl.txt)

2. Implement the TransitionSystem for the new DSL to (1) convert the programs between logical forms and ASTs (2) check if a partial program (incomplete AST) is correct during beam search decoding. Refer to grammar/turk/turk_trainsition_system.py.

3. Modify the evaluation code accordingly.

Credit

Part of the codes and system design are modified from TranX.