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. |
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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 . |
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Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL (2022.coling-1)
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| Challenge: | Existing attempts on Text-to-SQL task show a dramatic decline in performance for new databases. |
| Approach: | They propose a hybrid system that integrates rule-based and deep learning components to improve model accuracy. |
| Outcome: | The proposed system achieves double-digit percentage improvement for non-Spider databases. |
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. |
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Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)
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Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev
| Challenge: | Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models. |
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DuoRAT: Towards Simpler Text-to-SQL Models (2021.naacl-main)
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| Challenge: | Recent text-to-SQL models can translate natural language questions to corresponding SQL queries on unseen databases. |
| Approach: | They propose a re-implementation of the RAT-SQL model that uses only relation-aware or vanilla transformers as the building blocks. |
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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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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. |
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A Review of Cross-Domain Text-to-SQL Models (2020.aacl-srw)
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| Challenge: | WikiSQL and Spider are cross-domain text-to-SQl datasets that have attracted much attention from the research community. |
| Approach: | They propose to divide top models into two paradigms and evaluate their models for schema linking, pretrained word embeddings, reasoning assistance modules. |
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SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (D18-1)
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| Challenge: | Existing studies in text-to-SQL do not require generating complex SQL queries with multiple clauses or sub-queries. |
| Approach: | They propose a syntax tree network to address the complex text-to-SQL generation task. |
| Outcome: | The proposed model outperforms the current state-of-the-art model by 9.5% on a large text-to-SQL corpus. |
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%. |