RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (2020.acl-main)
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| Challenge: | Existing semantic parsing models struggle to generalize to unseen database schemas. |
| Approach: | They propose a framework to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. |
| Outcome: | The proposed framework boosts the match accuracy to 57.2% on the spider dataset, surpassing its best counterparts by 8.7%. |
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| Challenge: | Recent work has shown that schema linking reduces robustness of text-to-SQL models . EMSL is used to correlate natural language queries with the given database schema . |
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AmirHossein Safdarian, Milad Mohammadi, Ehsan Jahanbakhsh Bashirloo, Mona Shahamat Naderi, Heshaam Faili
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| Challenge: | Existing text-to-SQL models are limited in their generalizability, despite their performance being over-estimated. |
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| Challenge: | Existing text-to-SQL approaches have overlooked the critical aspect of system robustness. |
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Re-examining the Role of Schema Linking in Text-to-SQL (2020.emnlp-main)
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| Challenge: | Existing text-to-SQL models treat schema linking as a minor component . Existing solutions treat schema as merely a string component based on string matching . |
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| Challenge: | Existing text-to-SQL parsers struggle with out-of-domain generalization problems, arguing that they lack the ability to match domain specific phrases to composite operations over columns. |
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