R²SQL

The PyTorch implementation of paper Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing. (AAAI 2021)

Requirements

The model is tested in python 3.6 with following requirements:

torch==1.0.0
transformers==2.10.0
sqlparse
pymysql
progressbar
nltk
numpy
six
spacy

All experiments on SParC and CoSQL datasets were run on NVIDIA V100 GPU with 32GB GPU memory.

• Tips: The 16GB GPU memory may appear out-of-memory error.

Setup

The SParC and CoSQL experiments in two different folders, you need to download different datasets from [SParC | CoSQL] to the {sparc|cosql}/data folder separately. Another related data file could be download from EditSQL. Then, download the database sqlite files from [here] as data/database.

Download Pretrained BERT model from [here] as model/bert/data/annotated_wikisql_and_PyTorch_bert_param/pytorch_model_uncased_L-12_H-768_A-12.bin.

Download Glove embeddings file (glove.840B.300d.txt) and change the GLOVE_PATH for your own path in all scripts.

Download Reranker models from [SParC reranker | CoSQL reranker] as submit_models/reranker_roberta.pt

Usage

Train the model from scratch.

./sparc_train.sh

Test the model for the concrete checkpoint:

./sparc_test.sh

then the dev prediction file will be appeared in results folder, named like save_%d_predictions.json.

Get the evaluation result from the prediction file:

./sparc_evaluate.sh

the final result will be appeared in results folder, named *.eval.

Similarly, the CoSQL experiments could be reproduced in same way.

You could download our trained checkpoint and results in here:

• SParC: [log | results]
• CoSQL: [log | results]

Reranker

If your want train your own reranker model, you could download the training file from here:

• SParC: [reranker training data]
• CoSQL: [reranker training data]

Then you could train, test and predict it:

train:

python -m reranker.main --train --batch_size 64 --epoches 50

test:

python -m reranker.main --test --batch_size 64

predict:

python -m reranker.predict

Improvements

We have improved the origin version (descripted in paper) and got more performance improvements 🥳 !

Compare with the origin version, we have made the following improvements：

• add the self-ensemble strategy for prediction, which use different epoch checkpoint to get final result. In order to easily perform this strategy, we remove the task-related representation in Reranker module.
• remove the decay function in DCRI, we find that DCRI is unstable with decay function, so we let DCRI degenerate into vanilla cross attention.
• replace the BERT-based with RoBERTa-based model for Reranker module.

The final performance comparison on dev as follows:

Citation

Please star this repo and cite paper if you want to use it in your work.

Acknowledgments

This implementation is based on "Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions" EMNLP 2019.