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https://github.com/guotong1988/NL2SQL-RULE
Content Enhanced BERT-based Text-to-SQL Generation https://arxiv.org/abs/1910.07179
https://github.com/guotong1988/NL2SQL-RULE
bert deep-learning knowledge knowledge-representation nl2sql nlp pytorch rule-inject-to-model semantic-parsing text2sql
Last synced: 2 days ago
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Content Enhanced BERT-based Text-to-SQL Generation https://arxiv.org/abs/1910.07179
- Host: GitHub
- URL: https://github.com/guotong1988/NL2SQL-RULE
- Owner: guotong1988
- Created: 2019-10-18T00:54:02.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-10-17T01:27:21.000Z (27 days ago)
- Last Synced: 2024-10-19T03:00:39.608Z (25 days ago)
- Topics: bert, deep-learning, knowledge, knowledge-representation, nl2sql, nlp, pytorch, rule-inject-to-model, semantic-parsing, text2sql
- Language: Python
- Homepage:
- Size: 6.34 MB
- Stars: 188
- Watchers: 7
- Forks: 48
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-nlp-note - NL to SQL by BERT
README
# NL2SQL-RULE
[](https://github.com/996icu/996.ICU/blob/master/LICENSE)
[](https://996.icu)
Content Enhanced BERT-based Text-to-SQL Generation https://arxiv.org/abs/1910.07179
# Motivation
### REASONABLE: Incorporating database design rule into text-to-sql generation:
1. We use the matching information of the table cells and question string to construct a vector where
its length is the same to the question length. This question vector mainly improves the performance
of WHERE-VALUE inference result. Because it injects the knowledge that the answer cell and its
corresponding table header are bound together. **If we locate the answer cell then we locate the answer
column which contains the answer cell.**
2. We use the matching information of all the table headers and the question string to construct a
vector where its length is the same to the table headers’ length. This header vector mainly improves the
performance of WHERE-COLUMN inference result.
# Requirements
python 3.6
torch 1.1.0
# Run
### Step-1
Data prepare: Download all origin data( https://drive.google.com/file/d/1iJvsf38f16el58H4NPINQ7uzal5-V4v4 or https://download.csdn.net/download/guotong1988/13008037) and put them at `data_and_model` directory.
Then run `data_and_model/output_entity.py`
### Step-2
Train and eval: `train.py`
# Results on BERT-Base-Uncased without Execution-Guided-Decoding
| **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 Method | 84.3 | 90.3 | 83.7 | 89.2 |
# Data
One data view:
```
{
"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]
}
```
The Corresponding Table:
```
{
"id": "1-1000181-1",
"header": ["State/territory", "Text/background colour", "Format", "Current slogan", "Current series", "Notes"],
"rows": [
["Australian Capital Territory", "blue/white", "Yaa·nna", "ACT · CELEBRATION OF A CENTURY 2013", "YIL·00A", "Slogan screenprinted on plate"],
["New South Wales", "black/yellow", "aa·nn·aa", "NEW SOUTH WALES", "BX·99·HI", "No slogan on current series"],
["New South Wales", "black/white", "aaa·nna", "NSW", "CPX·12A", "Optional white slimline series"],
["Northern Territory", "ochre/white", "Ca·nn·aa", "NT · OUTBACK AUSTRALIA", "CB·06·ZZ", "New series began in June 2011"],
["Queensland", "maroon/white", "nnn·aaa", "QUEENSLAND · SUNSHINE STATE", "999·TLG", "Slogan embossed on plate"],
["South Australia", "black/white", "Snnn·aaa", "SOUTH AUSTRALIA", "S000·AZD", "No slogan on current series"],
["Victoria", "blue/white", "aaa·nnn", "VICTORIA - THE PLACE TO BE", "ZZZ·562", "Current series will be exhausted this year"]
]
}
```
# Trained model
https://drive.google.com/open?id=18MBm9qzobTBgWPZlpA2EErCQtsMhlTN2
# Reference
https://github.com/naver/sqlova