SPARTA (Semantic Parsing And Relational Table Aware)

This is a term project in Unstructured Text Analysis class. We implement the deep learning model for converting Korean language to SQL query.

Team Members

Hoonsang Yoon
Jaehyuk Heo
Jungwoo Choi
Jeongseob Kim

Information

Korea University DSBA Lab
Advisor: Pilsung Kang

Demo

Check about Demo in here.

Video

Text2SQL Result Video

Dataset

WikiSQL

tar xvjf data/data.tar.bz2

Korean WikiSQL dataset

unzip data/ko_token.zip
unzip data/ko_token_not_h.zip
unzip data/ko_from_table.zip
unzip data/ko_from_table_not_h.zip

Translation

We translated English question into Korean question in four ways as follows.

Download dataset

No	Method	Data Name	Description
1	Where+Select	ko_token	Keep `where values` in label and `column` used in select clause among the words in English question
2	Where	ko_token_not_h	Keep `header` of table among the words in English question
3	Table+Header	ko_from_table	Keep `values` and `header` in table among the words in English question
4	Table	ko_from_table_not_h	Keep `values` in table among the words in English question

Method 1 (Where+Select)

Method 2 (Where)

Method 3 (Table+Header)

Method 4 (Table)

Run translation

Create a question dataframe to translate English to Korean.

bash run_translate.sh value

Translate English to Korean by using Google Tanslator (click here!) and copy a text file in ko_data directory such as 'ko_train_question.txt'
Insert Korean question

bash run_translate.sh token

SPARTA Model

We use pretrained multilingual BERT as encoder.

Sub Task

SQLova [ paper | github ]
HydraNet [ paper | github ]

Seq2Seq

BRIDGE(TabularSemanticParsing)[ paper | github ]

Evaluation

Logical Form Accuracy
Execution Accuracy

Experiments

Model	Task	Test Logical Form Accuracy(%)	Test Execution Accuracy(%)
SQLova	Subtask	65.8	74.3
HydraNet	Subtask	40.4	40.7
Bridge	Generation	54.6	62.1

Download Trained Models

Method	SQlova	Bridge
Where+Select	Download	-
Where	Download	-
Table+Header	Download	-
Table	Download	-

Presentation

Proposal

Interim Findings

Final

Reference

[1] Victor Zhong, Caiming Xiong, and Richard Socher. 2017. Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning.
[2] Hwang, W., Yim, J., Park, S., & Seo, M. (2019). A comprehensive exploration on wikisql with table-aware word contextualization. KR2ML Workship at NeurIPS 2019
[3] Lyu, Q., Chakrabarti, K., Hathi, S., Kundu, S., Zhang, J., & Chen, Z. (2020). Hybrid ranking network for text-to-sql. arXiv preprint arXiv:2008.04759.
[4] Xi Victoria Lin, Richard Socher and Caiming Xiong. Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing. Findings of EMNLP 2020.

TooTouch/SPARTA