Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing (2020.acl-main)
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| Challenge: | Existing evaluation datasets such as Spider are used to support cross-database semantic parsing . XSP systems that map natural language utterances to SQL queries are evaluated on databases unseen during training. |
| Approach: | They propose a setup that uses eight well-studied datasets to evaluate cross-database semantic parsing systems. |
| Outcome: | The proposed system performs well on spider, but struggles to generalize to the repurposed set. |
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| Challenge: | Existing models for cross-lingual semantic parsing are not able to perform tasks on a wide range of datasets. |
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| Challenge: | Existing approaches to semantic parsing only evaluated on synthetic datasets that are not representative of natural language variation. |
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| Challenge: | Text-to-SQL parsers must be generalizable and robust against input perturbations. |
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| Challenge: | Existing text-to-SQL models do not generalize when faced with domain knowledge that does not frequently appear in training data. |
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| Challenge: | Recent large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQl parsing. |
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