mlsanityData to 7 Oct 2026

T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing

arXiv 2306.08368 ↗First posted 14 Jun 2023cs.CL cs.AIcited by 2

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Abstract (arXiv)

Translating natural language queries into SQLs in a seq2seq manner has attracted much attention recently. However, compared with abstract-syntactic-tree-based SQL generation, seq2seq semantic parsers face much more challenges, including poor quality on schematical information prediction and poor semantic coherence between natural language queries and SQLs. This paper analyses the above difficulties and proposes a seq2seq-oriented decoding strategy called SR, which includes a new intermediate representation SSQL and a reranking method with score re-estimator to solve the above obstacles respectively. Experimental results demonstrate the effectiveness of our proposed techniques and T5-SR-3b achieves new state-of-the-art results on the Spider dataset.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 24 references that are in the map. First posted on arXiv 14 Jun 2023.

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22 of 24 references.

References · 22

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  3. 2015Pointer Networkscited by 882
  4. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  5. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  6. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  7. 2016Incorporating Copying Mechanism in Sequence-to-Sequence Learningcited by 217
  8. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  9. 2016Language to Logical Form with Neural Attentioncited by 158
  10. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
  11. 2020Structure-Grounded Pretraining for Text-to-SQLcited by 126
  12. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
10 more
  1. 2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
  2. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  3. 2018SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Taskcited by 74
  4. 2021Natural SQL: Making SQL Easier to Infer from Natural Language Specificationscited by 66
  5. 2020Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Trainingcited by 65
  6. 2022S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parserscited by 41
  7. 2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parsercited by 35
  8. 2020IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generationcited by 29
  9. 2022HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsingcited by 26
  10. 2022UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQLcited by 20

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Citing papers per year: 2025: 2.

2 papers.

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