| Challenge: | Existing text-to-SQL systems take a slot-filling approach, but they are limited in capturing inter-dependencies among SQL clauses. |
| Approach: | They propose an extraction-linking approach where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries. |
| Outcome: | The proposed method achieves the first place on the WikiSQL benchmark. |
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| Challenge: | Text-to-SQL systems can generate SQL queries given natural language questions. |
| Approach: | They propose a method that formulates a question answering problem as a query answering problem where different slots are predicted by a unified machine reading comprehension (MRC) model. |
| Outcome: | The proposed method can achieve competitive results on WikiSQL, suggesting it being a promising direction for text-to-SQl. |
Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)
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Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou
| Challenge: | Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents. |
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TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation (N18-2)
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| Challenge: | Existing systems that can understand natural language questions and generate corresponding SQL queries are not able to do this. |
| Approach: | They propose a novel approach which formats the problem as a slot filling task in a more reasonable way and utilizes type information to better understand rare entities and numbers in the questions. |
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Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQL (2026.findings-eacl)
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| Challenge: | Recent methods focus on improving SQL generation but neglect retrieval of relevant schema elements. |
| Approach: | They propose a context-aware bidirectional schema retrieval framework that treats schema linking as a standalone problem. |
| Outcome: | The proposed framework improves schema recall while reducing false positives. |
SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising (2022.findings-naacl)
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| Challenge: | Using sketch-based slot filling, text-to-SQL models suffer from over-complexity . et al., e.al., and d.albert, dr., propose a novel method for text- to-Sql generation . |
| Approach: | They propose to train sequence-to-sequence model with Schema-aware Denoising . they propose a clause-sensitive execution guided (EG) decoding strategy . |
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Hyper-QKSG: Framework for Automating Query Generation and Knowledge-Snippet Extraction from Tables and Lists (2024.emnlp-industry)
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| Challenge: | Featured snippets are a compressed excerpt that contains the answer to a user's query . knowledge-snippet is a useful tool for generating information retrieval services such as google. |
| Approach: | They propose to automatically extract query-knowledge snippet pairs from structured HTML documents using a new Language Model. |
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Event Extraction by Answering (Almost) Natural Questions (2020.emnlp-main)
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| Challenge: | Existing work in event argument extraction relies heavily on entity recognition as a preprocessing/concurrent step, causing error propagation. |
| Approach: | They propose a question answering task that extracts event arguments in an end-to-end manner. |
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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. |
| Approach: | They propose to use schema linking and structural linking to link NL to the database schema. |
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Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion (2022.acl-long)
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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. |
| Approach: | They propose to use a synthetic dataset and a re-purposed train/test split to quantify out-of-domain generalization over column operations to address this problem. |
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Re-appraising the Schema Linking for Text-to-SQL (2023.findings-acl)
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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 . |
| Approach: | They propose a grammar linking module to help model align grammar references with SQL keywords. |
| Outcome: | The proposed language model improves performance without using EMSL, the authors show . their language model is more robust, and the proposed grammar linking improves interoperability . |