tensor2struct is a package contains a set of neural semantic parsers based on encoder-decoder framework. Currently, it contains the code and data for the following papers:
- Meta-Learning for Domain Generalization in Semantic Parsing, NAACL 2021
- Learning from Executions for Semantic Parsing, NAACL 2021
- Learning to Synthesize Data for Semantic Parsing, NAACL 2021
Create a virtual environment and run the setup script.
conda create --name tensor2struct python=3.7
conda activate tensor2struct
./setup.sh
wandb is used for logging. To enable it you can create your own account and wandb login
to enable logging.
Or you could just wandb off
to only allow dryrun locally.
In general, the raw data is expected to be placed under "/data/TASK_NAME/raw" where TASK_NAME could be spider/ssp/overnight.
Make log/
and ie_dir/
which will be used for storing checkpoints and predictions (during inference).
- Meta-Learning for Domain Generalization
- Learning from Executions for Semantic Parsing
- Learning to Synthesize Data for Semantic Parsing
Tensor2struct is a generalization of RAT-SQL.