kaayy / tisp

Type-driven Incremental Semantic Parser

Type-driven Incremental Semantic Parser

This repository contains an implementation of the semantic parser in paper Type-driven Incremental Semantic Parsing with Polymorphism.

Required Dependencies

1. PyPy: due to the heavy computation required by this parser, it is strongly recommended to use PyPy instead of CPython. This program is tested with PyPy 5.4.0.
2. Python packages including:
3. python-gflags: used for command-line arguments parsing.
4. pyparsing: used for parsing lambda expressions in the dataset.

Training

To train on the provided sample data set and saving the model, you can run:

trainer.py --outputprefix exps/demotrain

where exps is a directory storing all training models, and demotrain is the prefix of the saved model file. The trainer will dump its weights to a standalone weight file (a pickle file) at each training iteration.

Evaluating

To evaluate the trained model on development set and testing set, you can run:

trainer.py --eval exps/demotrain.9.pickle

where exps/demotrain.9.pickle is a weight file saved in the previous training.

tisp's Issues

Full data-sets?

Would much appreciate the data on which results were reported in the paper (geoquery, jobs, atis), in the form in which they were used by the system. (That helps make clear what kind of pre-processing was done.) Thanks!