- SQLova is a neural semantic parser translating natural language utterance to SQL query. The name is originated from the name of our department: Search & QLova (Search & Clova).
- Wonseok Hwang, Jinyeong Yim, Seunghyun Park, and Minjoon Seo.
- Affiliation: Clova AI Research, NAVER Corp., Seongnam, Korea.
- The updated version of manuscript is available from arXiv.
- The manuscript is significantly re-written to improve readability.
- The detailed description of the model and human evaluation process have added.
- To be presented at KR2ML Workshop at NeurIPS 2019.
- The old version.
- We present the new state-of-the-art semantic parsing model that translates a natural language (NL) utterance into a SQL query.
- The model is evaluated on WikiSQL, a semantic parsing dataset consisting of 80,654 (NL, SQL) pairs over 24,241 tables from Wikipedia.
- We achieve 83.6% logical form accuracy and 89.6% execution accuracy on WikiSQL test set.
- BERT based table- and context-aware word-embedding.
- The sequence-to-SQL model leveraging recent works (Seq2SQL, SQLNet).
- Execution-guided decoding is applied in SQLova-EG.
| Model | Dev logical form accuracy |
Dev execution accuracy |
Test logical form accuracy |
Test execution accuracy |
|---|---|---|---|---|
| SQLova | 81.6 (+5.5)^ | 87.2 (+3.2)^ | 80.7 (+5.3)^ | 86.2 (+2.5)^ |
| SQLova-EG | 84.2 (+8.2)* | 90.2 (+3.0)* | 83.6(+8.2)* | 89.6 (+2.5)* |
- ^: Compared to current SOTA models that do not use execution guided decoding.
- *: Compared to current SOTA.
- The order of where conditions is ignored in measuring logical form accuracy in our model.
python3.6or higher.PyTorch 0.4.0or higher.CUDA 9.0- Python libraries:
babel, matplotlib, defusedxml, tqdm - Example
- Install minicoda
conda install pytorch torchvision -c pytorchconda install -c conda-forge records==0.5.2conda install babelconda install matplotlibconda install defusedxmlconda install tqdm
- The code has been tested on Tesla M40 GPU running on Ubuntu 16.04.4 LTS.
- Type
python3 train.py --seed 1 --bS 16 --accumulate_gradients 2 --bert_type_abb uS --fine_tune --lr 0.001 --lr_bert 0.00001 --max_seq_leng 222on terminal.--seed 1: Set the seed of random generator. The accuracies changes by few percent depending onseed.--bS 16: Set the batch size by 16.--accumulate_gradients 2: Make the effective batch size be16 * 2 = 32.--bert_type_abb uS: Uncased-Base BERT model is used. UseuLto use Uncased-Large BERT.--fine_tune: Train BERT. Without this, only the sequence-to-SQL module is trained.--lr 0.001: Set the learning rate of the sequence-to-SQL module as 0.001.--lr_bert 0.00001: Set the learning rate of BERT module as 0.00001.--max_seq_leng 222: Set the maximum number of input token lengths of BERT.
- The model should show ~79% logical accuracy (lx) on dev set after ~12 hrs (~10 epochs). Higher accuracy can be obtained with longer training, by selecting different seed, by using Uncased Large BERT model, or by using execution guided decoding.
- Add
--EGargument while runningtrain.pyto use execution guided decoding. - Whenever higher logical form accuracy calculated on the dev set, following three files are saved on current folder:
model_best.pt: the checkpoint of the the sequence-to-SQL module.model_bert_best.pt: the checkpoint of the BERT module.results_dev.jsonl: json file for official evaluation.
Shallow-LayerandDecoder-Layermodels can be trained similarly (train_shallow_layer.py,train_decoder_layer.py).
- To calculate logical form and execution accuracies on
devset using official evaluation script,- Download original WikiSQL dataset.
- tar xvf data.tar.bz2
- Move them under
$HOME/data/WikiSQL-1.1/data - Set path on
evaluation_ws.py. This is the file where the path information has added on originalevaluation.pyscript. Or you can use originalevaluation.pyby setting the path to the files by yourself. - Type
python3 evaluation_ws.pyon terminal.
- Uncomment line 550-557 of
train.pyto loadtest_loaderandtest_table. - One
test(...)function, usetest_loaderandtest_tableinstead ofdev_loaderanddev_table. - Save the output of
test(...)withsave_for_evaluation(...)function. - Evaluate with
evaluatoin_ws.pyas before.
- Pretrained SQLova model parameters are uploaded in release. To start from this, uncomment line 562-565 and set paths.
- Pretrained BERT models were downloaded from official repository.
- BERT code is from huggingface-pytorch-pretrained-BERT.
- The sequence-to-SQL model is started from the source code of SQLNet and significantly re-written while maintaining the basic column-attention and sequence-to-set structure of the SQLNet.
- The data is annotated by using
annotate_ws.pywhich is based onannotate.pyfrom WikiSQL repository. The tokens of natural language guery, and the start and end indices of where-conditions on natural language tokens are annotated. - Pre-trained BERT parameters can be downloaded from BERT official repository and can be coverted to
ptfile using following script. You need install both pytorch and tensorflow and changeBERT_BASE_DIRto your data directory.
cd sqlova
export BERT_BASE_DIR=data/uncased_L-12_H-768_A-12
python bert/convert_tf_checkpoint_to_pytorch.py \
--tf_checkpoint_path $BERT_BASE_DIR/bert_model.ckpt \
--bert_config_file $BERT_BASE_DIR/bert_config.json \
--pytorch_dump_path $BERT_BASE_DIR/pytorch_model.bin bert/convert_tf_checkpoint_to_pytorch.pyis from the previous version of huggingface-pytorch-pretrained-BERT, and current version ofpytorch-pretrained-BERTis not compatible with the bert model used in this repo due to the difference in variable names (in LayerNorm). See this for the detail.- For the convenience, the annotated WikiSQL data and the PyTorch-converted pre-trained BERT parameters are available at here.
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