| Challenge: | despite of its simplicity, none of the publicly reported structured query generation models can achieve an accuracy beyond 62%, which is far from enough for practical use. |
| Approach: | They propose a model that can achieve 88.6% condition accuracy on WikiSQL . they ask: why is the accuracy still low for such simple queries? |
| Outcome: | The proposed solution can reach up to 88.6% condition accuracy on the WikiSQL dataset. |
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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. |
TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation (N18-2)
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| Challenge: | Existing systems that can understand natural language questions and generate corresponding SQL queries are not able to do this. |
| Approach: | They propose a novel approach which formats the problem as a slot filling task in a more reasonable way and utilizes type information to better understand rare entities and numbers in the questions. |
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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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Searching for Better Database Queries in the Outputs of Semantic Parsers (2023.findings-eacl)
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| Challenge: | generating a database query from a question in natural language is a longstanding task . the task is amplified when the system needs to generalize to databases unseen at training. |
| Approach: | They propose to generalize a query to databases unseen at training . they use state-of-the-art semantic parsers to find queries that meet the criterion . |
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Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)
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| Challenge: | Using question generation, we learn a semantic parser with 30% of the supervised training data. |
| Approach: | They propose to use question generation to learn a semantic parser with less supervised training data. |
| Outcome: | The proposed method improves the state-of-the-art model with less training data. |
DialSQL: Dialogue Based Structured Query Generation (P18-1)
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| Challenge: | Recent advances in deep learning and semantic parsing have improved the translation accuracy of natural language questions to structured queries. |
| Approach: | They propose a dialogue-based structured query generation framework that leverages human intelligence to boost performance of existing algorithms via user interaction. |
| Outcome: | The proposed framework improves on a WikiSQL dataset from 61.3% to 69.0% using only 2.4 validation questions per dialogue. |
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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Natural SQL: Making SQL Easier to Infer from Natural Language Specifications (2021.findings-emnlp)
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| Challenge: | Existing models that do not support executable SQL generation can generate executable queries. |
| Approach: | They propose an SQL intermediate representation called Natural SQL (NatSQL) they propose to preserve the core functionalities of SQL while simplifying the queries . |
| Outcome: | The proposed model outperforms existing models on a text-to-SQL benchmark . it significantly improves the performance of previous models on the same dataset . |
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. |
| Outcome: | The proposed model is based on the spider dataset and shows it can be used on large databases without human intervention. |
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion (2022.acl-long)
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| Challenge: | Existing text-to-SQL parsers struggle with out-of-domain generalization problems, arguing that they lack the ability to match domain specific phrases to composite operations over columns. |
| Approach: | They propose to use a synthetic dataset and a re-purposed train/test split to quantify out-of-domain generalization over column operations to address this problem. |
| Outcome: | The proposed method outperforms baseline parsers on the domain generalization problem, while boosting the underlying parser’ overall performance by 13.8% relative accuracy gain (5.1% absolute). |