Content Enhanced BERT-based Text-to-SQL Generation https://arxiv.org/abs/1910.07179
(Incorporating database designing info into text-to-sql generation)
python 3.6
records 0.5.3
torch 1.1.0
1, Data prepare:
Download all origin data( https://drive.google.com/file/d/1iJvsf38f16el58H4NPINQ7uzal5-V4v4 ) and put them at data_and_model directory.
Then run
data_and_model/output_entity.py
2, Train and eval:
train.py
| Model | Dev logical form accuracy |
Dev execution accuracy |
Test logical form accuracy |
Test execution accuracy |
|---|---|---|---|---|
| SQLova | 80.6 | 86.5 | 80.0 | 85.5 |
| Our Methods | 84.3 | 90.3 | 83.7 | 89.2 |
One data look:
{
"table_id": "1-1000181-1",
"phase": 1,
"question": "Tell me what the notes are for South Australia ",
"question_tok": ["Tell", "me", "what", "the", "notes", "are", "for", "South", "Australia"],
"sql": {
"sel": 5,
"conds": [
[3, 0, "SOUTH AUSTRALIA"]
],
"agg": 0
},
"query": {
"sel": 5,
"conds": [
[3, 0, "SOUTH AUSTRALIA"]
],
"agg": 0
},
"wvi_corenlp": [
[7, 8]
],
"bertindex_knowledge": [0, 0, 0, 0, 4, 0, 0, 1, 3],
"header_knowledge": [2, 0, 0, 2, 0, 1]
}
https://drive.google.com/open?id=18MBm9qzobTBgWPZlpA2EErCQtsMhlTN2