Challenge: Existing approaches to complex and cross-domain Text-to-SQL generation lack domain knowledge . domain knowledge is not incorporated to enhance their ability to generalise to unseen databases.
Approach: They propose a framework called G3R for complex and cross-domain Text-to-SQL generation . they propose re-ranking SQL queries based on domain knowledge and a graph-guided SQL generator .
Outcome: The proposed framework achieves state-of-the-art results on the Spider and Spider-DK benchmarks.

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Graph Enhanced Cross-Domain Text-to-SQL Generation (D19-53)

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Challenge: Existing deep learning approaches for semantic parsing do not generalize to unseen data sets . existing benchmarks have shown text-to-SQL parsers do not generally perform well to unsen SQL queries.
Approach: They propose a new cross-domain learning scheme to perform text-to-SQL translation . they demonstrate its use on a large-scale cross- domain text- to-Sql data set Spider .
Outcome: The proposed learning scheme improves on a large-scale text-to-SQL data set.
Bi-Directional Multi-Granularity Generation Framework for Knowledge Graph-to-Text with Large Language Model (2024.acl-short)

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Challenge: Existing methods generate whole text based on all KG triples at once and may incorporate incorrect KG Triples for each sentence.
Approach: They propose a bi-directional multi-granularity generation framework that generates graph-level sentences based on KG triples instead of the whole text at a time.
Outcome: The proposed framework achieves state-of-the-art in benchmark dataset WebNLG and further analysis shows the efficiency of different modules.
PG-GSQL: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-SQL Generation (2020.coling-main)

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Challenge: Existing approaches to text-to-SQL generation depend on interaction history and current utterances.
Approach: They propose an encoder-decoder model based on interaction-level encoder to capture historical information of SQL query and reuse the previous SQL query tokens.
Outcome: The proposed model outperforms the previous state-of-the-art model on the SParC benchmark . it achieves 34.0% question matching accuracy and 19.0% interaction matching accuracy .
Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency (2022.findings-naacl)

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Challenge: Existing knowledge graph-to-text generation methods focus on sequence-to sequence generation, but the linearized order of KG is obtained through a heuristic search without data-driven optimization.
Approach: They propose to generate easy-to-understand sentences from the knowledge graph . they incorporate part-of-speech syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context.
Outcome: The proposed method achieves state-of-the-art on two datasets, WebNLG and DART, and achieves high consistency.
ReFSQL: A Retrieval-Augmentation Framework for Text-to-SQL Generation (2023.findings-emnlp)

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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.
R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL (2024.findings-emnlp)

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Challenge: Adapting existing approaches for converting natural language to SQL encounters hurdles due to distinct nature of GQL compared to SQL.
Approach: They propose a method that integrates both small and large Foundation Models for ranking, rewriting, and refining tasks.
Outcome: The proposed approach integrates both small and large Foundation Models for ranking, rewriting, and refining tasks while capitalizing on the superior generalization and query generation prowess of larger models for the final transformation of natural language queries into GQL formats.
Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation (D19-1)

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Challenge: Existing deep learning approaches for text-to-SQL generation are limited to the WikiSQl dataset . a novel clause-wise decoding neural network model can be used to generate complex queries over multiple databases .
Approach: They propose a SQL clause-wise decoding neural architecture with a schema encoder to address the Spider task.
Outcome: The proposed model achieves 4.6% accuracy gain on the Spider dataset and 9.8% accuracy gain in test and dev sets.
SQL-to-Text Generation with Graph-to-Sequence Model (D18-1)

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Challenge: Existing approaches to generate SQL-to-text using seq2seq models do not capture graph-structured information in SQL query.
Approach: They propose a graph-to-sequence model to encode global structure information into node embeddings.
Outcome: The proposed model outperforms the Seq2Seq and Tree2Sq baselines on the WikiSQL and Stackoverflow datasets.
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation (P19-1)

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Challenge: IRNet synthesizes SQL queries in an end-to-end manner, but it yields unsatisfactory performance on public benchmarks.
Approach: They propose a neural approach called IRNet for complex and cross-domain Text-to-SQL.
Outcome: IRNet achieves 46.7% accuracy on the Spider benchmark, a 19.5% improvement over state-of-the-art approaches.
MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation (2025.naacl-long)

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Challenge: Recent advances in text-to-SQL generation rely on large closed-source models that present challenges in accessibility, privacy, and latency.
Approach: They propose to use open-source text-to-SQL models to critique SQL queries . their method evaluates multiple outputs simultaneously and is competitive with larger models .
Outcome: The proposed method achieves state-of-the-art performance compared to open-source models while remaining competitive with larger models at a much lower cost.

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