| Challenge: | Existing semantic parsers only select a set of database constants at training time . current models only consider local information, not global ones . |
| Approach: | They propose a semantic parser that globally reasons about the structure of the query to make a more contextually-informed selection of database constants. |
| Outcome: | The proposed model increases accuracy from 39.4% to 47.4% on a zero-shot semantic parsing dataset with complex databases. |
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Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Bing Xiang, Ramesh Nallapati, Sudipta Sengupta
| Challenge: | Recent research on Text-to-SQL semantic parsing relies on parser or heuristic based approach to understand natural language query. |
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Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing (P19-1)
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| Challenge: | Semantic parsing to SQL has largely ignored the structure of the database schema . a recent study used a simple DB that was observed at both training and test time. |
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A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing (2020.coling-main)
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| Challenge: | Existing methods for text-to-SQL semantic parsing require strict structured prediction due to its application scenario where the output SQL will be sent to an executor program directly. |
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Compositional Semantic Parsing across Graphbanks (P19-1)
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| Challenge: | Existing semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. |
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Searching for Better Database Queries in the Outputs of Semantic Parsers (2023.findings-eacl)
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| Challenge: | generating a database query from a question in natural language is a longstanding task . the task is amplified when the system needs to generalize to databases unseen at training. |
| Approach: | They propose to generalize a query to databases unseen at training . they use state-of-the-art semantic parsers to find queries that meet the criterion . |
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Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database (2022.emnlp-main)
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| Challenge: | Existing approaches on semantic parsing suffer from exponential growth of logical form candidates and can hardly generalize to unseen data. |
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Error Detection for Text-to-SQL Semantic Parsing (2023.findings-emnlp)
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| Challenge: | Existing text-to-SQL parsers are often over-confident, thus casting doubt on their trustworthiness when deployed for real use. |
| Approach: | They propose a parser-independent error detection model for text-to-SQL semantic parsing . they use a language model of code as its bedrock and graph neural networks to learn structural features of queries . |
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Coarse-to-Fine Decoding for Neural Semantic Parsing (P18-1)
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| Challenge: | Experimental results show that semantic parsing is more efficient than using simple decoders. |
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A Pilot Study for Chinese SQL Semantic Parsing (D19-1)
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| Challenge: | Existing datasets to map natural language text into SQL are limited in their use in question-to-sql mapping. |
| Approach: | They propose to use a Chinese-based semantic parser to map natural language text into SQL. |
| Outcome: | The proposed dataset compares a character-based parser with a word embedding scheme for Chinese . the results show that the parsers are subject to segmentation errors and cross-lingual embedders are useful for text-to-SQL mapping. |
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. |