Papers with text-to-
Photon: A Robust Cross-Domain Text-to-SQL System (2020.acl-demos)
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Jichuan Zeng, Xi Victoria Lin, Steven C.H. Hoi, Richard Socher, Caiming Xiong, Michael Lyu, Irwin King
| Challenge: | Existing natural language interfaces to databases are ambiguous or untranslatable . we present a robust, modular cross-domain text-to-SQL system . |
| Approach: | They propose a system that flags natural language input to which a SQL mapping cannot be immediately determined. |
| Outcome: | The proposed system can flag natural language input to which a SQL mapping cannot be determined. |
SQL Generation via Machine Reading Comprehension (2020.coling-main)
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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. |
Improving Text-to-SQL Evaluation Methodology (P18-1)
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Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, Dragomir Radev
| Challenge: | Current evaluations of text-to-SQL systems are limited by the way they divide data into training and test sets. |
| Approach: | They propose to standardize and improve existing and new text-to-SQL datasets . they propose a template-based slot-filling baseline that cannot generalize to new queries . |
| Outcome: | The proposed system is competitive with prior work on multiple datasets and can be used on training and test sets. |
Towards Generalizable and Robust Text-to-SQL Parsing (2022.findings-emnlp)
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| Challenge: | Text-to-SQL parsers must be generalizable and robust against input perturbations. |
| Approach: | They propose a novel framework to learn text-to-SQL parsing in stages to improve parser's ability to acquire general SQL knowledge instead of capturing spurious patterns. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the Spider, SParC, and CoSQL datasets. |
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs (2022.findings-naacl)
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| Challenge: | Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations. |
| Approach: | They propose to use auxiliary tasks which are semantically or formally related to enhance AMR parsing. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmarks especially in topology-related scores. |
Towards Robustness of Text-to-SQL Models against Synonym Substitution (2021.acl-long)
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Yujian Gan, Xinyun Chen, Qiuping Huang, Matthew Purver, John R. Woodward, Jinxia Xie, Pengsheng Huang
| Challenge: | Existing text-to-SQL models rely on lexical matching between words in NL questions and tokens in table schemas, which may break the schema linking mechanism. |
| Approach: | They propose a human-curated dataset for text-to-SQL translation . they replace schema-related words with manually selected synonyms . |
| Outcome: | The proposed model outperforms its counterparts without the defense. |
FLEX: Expert-level False-Less EXecution Metric for Text-to-SQL Benchmark (2025.naacl-long)
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| Challenge: | Existing evaluation methods for text-to-SQL systems show many false positives and negatives . however, the Execution Accuracy (EX) metric is flawed and can diverge from human experts. |
| Approach: | They propose a method to evaluate text-to-SQL systems using large language models to emulate human expert-level evaluation of SQL queries. |
| Outcome: | The proposed metric improves agreement with human experts with comprehensive context and sophisticated criteria. |
Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systems (2025.findings-naacl)
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| Challenge: | Existing text-to-SQL systems rely on database schema information for SQL generation, exposing them to security vulnerabilities. |
| Approach: | They propose a framework for reconstructing database schema of text-to-SQL models without prior knowledge of the database. |
| Outcome: | The proposed framework achieves high accuracy in reconstructing table names, with F1 scores of up to .99 for generative models and .78 for fine-tuned models, underscoring the severity of schema leakage risks. |
Synthesizing Text-to-SQL Data from Weak and Strong LLMs (2024.acl-long)
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| Challenge: | a capability gap exists between open-source and closed-source large language models (LLMs) . the adoption of closed-sourced LLMs introduces concerns pertaining to openness, privacy, and substantial costs. |
| Approach: | They propose a synthetic data approach that combines strong and weak models for error information . they demonstrate the effectiveness of SENSE, a specialized text-to-SQL model . |
| Outcome: | The proposed method enhances the domain generalization of text-to-SQL models and explores the potential of error data supervision through preference learning. |
Mention Extraction and Linking for SQL Query Generation (2020.emnlp-main)
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| 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. |
T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation (2022.emnlp-main)
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| Challenge: | Unavailability of parallel corpora for training text style transfer models is a challenge but common . a large corpus of parallel data is not available for text style transfers . |
| Approach: | They propose to use AMR as an intermediate style agnostic representation to train TST models. |
| Outcome: | The proposed model outperforms state-of-the-art models in the style agnostic task. |
Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM (2024.findings-acl)
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| Challenge: | Existing methods for Generating accurate SQL queries for user questions rely on the capability of large language models (LLMs) however, some knowledge is not explicitly included in the database schema and user question or has been learned by LLMs. |
| Approach: | They propose a Knowledge-to-SQL framework that employs tailored Data Expert LLM (DELLM) to provide helpful knowledge for all text-to SQL models. |
| Outcome: | The proposed framework improves the state-of-the-art approaches for text-to-SQL tasks by leveraging a data expert LLM (DELLM) to provide useful knowledge for all text- to-SqL models. |
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization (2021.emnlp-main)
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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. |
| Approach: | They propose a human-curated dataset based on the Spider benchmark for text-to-SQL translation. |
| Outcome: | The proposed model performs better on unseen domains than existing models on public benchmarks. |
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking (2023.findings-emnlp)
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| Challenge: | Existing methods for zero-shot Dialogue State Tracking have focused on domaintransfers and have not yielded satisfactory results. |
| Approach: | They propose a new In-Context Learning method to introduce additional updating strategies in zero-shot DST by leveraging powerful Large Language Models and translating the original dialogue to JSON through semantic parsing as an intermediate state. |
| Outcome: | The proposed method outperforms existing zero-shot DST methods on MultiWOZ, showing significant improvements in JGA and slot accuracy compared to existing methods. |