Comments (1)
The original dataset from the 90s marked queries as requiring context or not and our data is derived from work at UW, which only used the examples that should not have required context. Unfortunately, there are some mistakes in the data that mean cases that require context are in our dataset (finding them and removing them or editing them is on the list of known issues in this repository).
Getting from our data back to the original should be possible by using approximate string matching (exact match won't work because we made some fixes to sentences, and because our variable extraction removed some variability, e.g. instead of sometimes having 'american airlines' and sometimes having 'aa', we always have 'aa').
If you are specifically interested in contextual SQL, check our Alane Suhr's work - https://github.com/clic-lab/atis and http://alanesuhr.com/atis.pdf
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Related Issues (20)
- Anonymised Variables should have consistent naming corresponding to their column HOT 4
- American vs UK spelling - Yelp Dataset HOT 1
- Hyperparameters for small datsets HOT 1
- Any canonicalization for quotes? HOT 2
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- Run sequence-to-sequence baseline models HOT 3
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- Spacing error(?) in geography.json HOT 4
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- Error for wikisql.json and spider.json while generating test,train and dev split HOT 9
- This is more of a question than issue. HOT 2
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- wrong primary keys in GeoQuery schema HOT 7
- location.restaurant_id should be foreign key in "Restaurants"? HOT 3
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- Wrong output of Geography using json_to_flat.py HOT 3
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