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The source code of paper "Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph" in KDD2022.
Nice work!
I have noticed that the oracle schema linking information could bring a huge improvement for Spider and Spider-SYN in section3.6, I wonder that where does the oracle schema information come from for both of two datasets. Could you offer a link please?
(I guess that the oracle schema information of Spider come from SLSQL(EMNLP2020), and how about Spider-SYN? )
Besides, could you please offer more details about how you directly change the implicit graph matrix A(𝑡) in Eq. 7 based on the oracle information, becaucse I want to try this by myself. Thanks a lot.
Best wishes!
Which script can be used for inference, i.e. loading the best model and then getting the final SQL file?
Nice work, how about the code?
Hey,
I tried following the setup instructions given in README.md but I think because of some changes in the Spider dataset these instructions are no longer valid.
For example,
python3 -u preprocess/process_dataset.py --dataset_path data/train.json --raw_table_path data/tables.json --table_path data/tables.bin --output_path 'data/train.bin' --skip_large --semantic_graph
There is no 'data/train.json' file in the Spider dataset. But it has 'data/train_spider.json' and 'data/train_others.json' files.
I tried changing the file name, but I get the following error.
Firstly, preprocess the original databases ... Traceback (most recent call last): File "/sensei-fs/users/saudi/text2sql/ISESL-SQL/preprocess/process_dataset.py", line 74, in <module> tables = process_tables(processor, tables_list, args.table_path, args.verbose) File "/sensei-fs/users/saudi/text2sql/ISESL-SQL/preprocess/process_dataset.py", line 26, in process_tables tables[each['db_id']] = processor.preprocess_database(each, verbose=verbose) File "/sensei-fs/users/saudi/text2sql/ISESL-SQL/preprocess/common_utils.py", line 100, in preprocess_database c = [w.lemma.lower() for s in doc.sentences for w in s.words] File "/sensei-fs/users/saudi/text2sql/ISESL-SQL/preprocess/common_utils.py", line 100, in <listcomp> c = [w.lemma.lower() for s in doc.sentences for w in s.words] AttributeError: 'NoneType' object has no attribute 'lower'
If possible, please upload the original dataset used in google drive and share the link.
Also, please provide the instructions to run the pipeline on a new database. Like what files need to be created in the data folder and what scripts to use.
Your help on this is much appreciated.
Best,
Saud Iqbal
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