SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing

This repository contains code for the ICLR 2021 paper "SCoRE: Pre-Training for Context Representation in Conversational Semantic Parsing".

If you use SCoRE in your work, please cite it as follows:

@inproceedings{yu2021SCoRE,
  title={{SCoRE}: Pre-Training for Context Representation in Conversational Semantic Parsing},
  author={Tao Yu and Rui Zhang and Oleksandr Polozov and Christopher Meek and Ahmed Hassan Awadallah},
  booktitle={International Conference on Learning Representations},
  year={2021},
  url={https://openreview.net/forum?id=oyZxhRI2RiE}
}

Environment Setup

At the time of development, we used the same environment setup as RAT-SQL. It assumes Python 3.7+ and CUDA 10.1. Thus, the simplest environment setup for all the experiments except SQA (find SQA's environment setup in sqa/README.md) is:

docker pull pytorch/pytorch:1.5-cuda10.1-cudnn7-devel
docker tag pytorch/pytorch:1.5-cuda10.1-cudnn7-devel score
docker run -it -v /path/to/this/repo:/workspace score
# or using GPUs
docker run --gpus 2 -it -v /path/to/this/repo:/workspace score

Run Experiments

Code and running commands for running all the experiments can be found in the following dirs. First, synthesize (or download) pre-training data and train a SCoRE checkpoint:

• data_synthesis: Synthesize Contextual Pre-Training Data
• SCoRE: Pre-Training SCoRE Using Synthesized Data

Then, to use the trained checkpoint as a base language model for conversational semantic parsing tasks:

• mwoz: SCoRE for Dialog State Tracking (MWoZ)
• sqa: SCoRE for Sequential Question Answering (SQA)
• sparc_cosql: SCoRE for Context-Dependent Semantic Parsing (SParC and CoSQL)

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

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