language:
- en
tags:
- text2sql
datasets:
- splash
widget:
- text: >-
Give the name, population, and head of state for the country that has the
largest area. || select name, population, continent from country order by
surfacearea desc limit 1 || | world_1 | city : id, name, countrycode,
district, population | sqlite_sequence : name, seq | country : code, name,
continent, region, surfacearea, indepyear, population, lifeexpectancy,
gnp, gnpold, localname, governmentform, headofstate, capital, code2 |
countrylanguage : countrycode, language, isofficial, percentage || swap
continent with head of state because it is not required.
parkervg/destt5-schema-prediction
Fine-tuned weights for the schema prediction model described in Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding, based on t5-large.
Training Data
The model has been fine-tuned on the 7,481 training examples in the SPLASH interactive semantic parsing dataset.
Training Objective
This model was initialized with t5-large and fine-tuned with the text-to-text generation objective.
As this model works in the interactive setting, we utilize the standard text2sql features such as question and db_schema, in addition to feedback and incorrect_parse.
[question] || [incorrect_parse] || [db_id] | [table] : [column] ( [content] , [content] ) , [column] ( ... ) , [...] | [table] : ... | ... || [feedback]
The model then attempts to predict those schema items that appear in the final gold SQL query, prefaced by the db_id.
[db_id] | [table] : [column] ( [content] , [content] ) , [column] ( ... ) , [...] | [table] : ...
Performance
This model achieves 88.98% F1 score in identifying schema items on the SPLASH test set.
When combined with the destt5-text2sql model, it achieves 53.43% correction accuracy (exact-match) on the SPLASH test set.
References
Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding
Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback
Citation
@inproceedings{glenn2023correcting,
author = {Parker Glenn, Parag Pravin Dakle, Preethi Raghavan},
title = "Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding",
booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI",
publisher = "Association for Computational Linguistics",
year = "2023"
}