| 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 . |
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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 . |
| Approach: | They build a schema linking corpus based on a Spider text-to-SQL dataset . they find schema linking is the crux for the current text- to-Sql task . |
| Outcome: | The proposed model performs well on the Spider text-to-SQL dataset despite its simplicity. |
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. |
| Outcome: | The proposed method shows significant gains on the Spider dataset. |
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. |
SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases (2026.findings-eacl)
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AmirHossein Safdarian, Milad Mohammadi, Ehsan Jahanbakhsh Bashirloo, Mona Shahamat Naderi, Heshaam Faili
| Challenge: | Text-to-SQL systems translate natural language questions into executable SQL queries. |
| Approach: | They propose a schema linking approach that first constructs a graph based on foreign key relations and then uses a single prompt to a lightweight LLM to extract source and destination tables from the user query. |
| Outcome: | The proposed method outperforms specialized, fine-tuned, and complex multi-step approaches on BIRD and Spider 2.0 benchmarks. |
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%. |
Exploring Schema Generalizability of Text-to-SQL (2023.findings-acl)
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| Challenge: | Existing text-to-SQL models are limited in their generalizability, despite their performance being over-estimated. |
| Approach: | They propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering. |
| Outcome: | The proposed framework generates text-to-SQL data via automatic and synchronous (DS, SQL) pair altering. |
Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL (2025.coling-main)
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| Challenge: | Existing text-to-SQL approaches have overlooked the critical aspect of system robustness. |
| Approach: | They propose a robust text-to-SQL solution that integrates with LLMs . their method achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% . |
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TP-Link: Fine-grained Pre-Training for Text-to-SQL Parsing with Linking Information (2024.lrec-main)
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Ziqiang Liu, Shujie Li, Zefeng Cai, Xiangyu Li, Yunshui Li, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Existing pre-training frameworks for text-to-SQL parsing have shown inherent differences in distributions between tables and plain text. |
| Approach: | They propose a framework to improve context-dependent Text-to-SQL parsing by leveraging Linking information. |
| Outcome: | The proposed framework achieves state-of-the-art performance on two leading downstream benchmarks. |
ReFSQL: A Retrieval-Augmentation Framework for Text-to-SQL Generation (2023.findings-emnlp)
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Kun Zhang, Xiexiong Lin, Yuanzhuo Wang, Xin Zhang, Fei Sun, Cen Jianhe, Hexiang Tan, Xuhui Jiang, Huawei Shen
| Challenge: | Existing methods that align natural language with SQL Language underestimate inherent structural characteristics of SQL and lead to structure errors. |
| Approach: | They propose a retrieval-argument framework that aligns natural language with SQL Language and trains one encoder-decoder-based model to fit all questions. |
| Outcome: | The proposed framework improves accuracy and robustness of text-to-SQL generation on five datasets. |
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect (2022.coling-1)
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| Challenge: | text-to-SQL is a language processing and database-based language processing (NLP) task is to convert natural utterances into SQL queries and its practical application is to build natural language interfaces to database systems. |
| Approach: | They propose to conduct a systematic survey of text-to-SQL to examine the challenges and potential future directions. |
| Outcome: | The proposed system converts natural utterances into SQL queries and is a representative task in semantic parsing. |