Automatic Generation and Reranking of SQL-Derived Answers to NL Questions

Giordani, Alessandra; Moschitti, Alessandro

doi:10.1007/978-3-642-45260-4_5

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…We also observe an array of that focus on generating SQL queries from natural language. Some of these datasets are synthetic (Zhong et al, 2017), mined from StackOverflow Hazoom et al, 2021) and Github , and human-curated (Tang and Mooney, 2000;Popescu et al, 2003;Giordani and Moschitti, 2012;Li and Jagadish, 2014;Iyer et al, 2017;Yu et al, 2018;Yaghmazadeh et al, 2017;Finegan-Dollak et al, 2018;Yu et al, 2019b). Map Question-Answering.…”

Section: Datasetsmentioning

confidence: 99%

A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective

Al-Hossami¹,

Shaikh²

2022

Preprint

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In this survey paper, we overview major deep learning methods used in Natural Language Processing (NLP) and source code over the last 35 years. Next, we present a survey of the applications of Artificial Intelligence (AI) for source code, also known as Code Intelligence (CI) and Programming Language Processing (PLP). We survey over 287 publications and present a software-engineering centered taxonomy for CI placing each of the works into one category describing how it best assists the software development cycle. Then, we overview the field of conversational assistants and their applications in software engineering and education. Lastly, we highlight research opportunities at the intersection of AI for code and conversational assistants and provide future directions for researching conversational assistants with CI capabilities.

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Section: Datasetsmentioning

confidence: 99%

A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective

Al-Hossami¹,

Shaikh²

2022

Preprint

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“…The original GEO dataset contains 880 English questions about US geography, paired with a meaning representation. Several MR formalisms have been introduced for this dataset, including a firstorder logic as in Zelle and Mooney (1996), a variable-free functional language introduced by Kate et al (2005) and SQL (Popescu et al, 2003;Giordani and Moschitti, 2013;Iyer et al, 2017).…”

Section: Alignment Annotationmentioning

confidence: 99%

Measuring Alignment Bias in Neural Seq2seq Semantic Parsers

Locatelli¹

2022

Proceedings of the 11th Joint Conference on Lexical and Computational Semantics

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Prior to deep learning the semantic parsing community has been interested in understanding and modeling the range of possible word alignments between natural language sentences and their corresponding meaning representations. Sequence-to-sequence models changed the research landscape suggesting that we no longer need to worry about alignments since they can be learned automatically by means of an attention mechanism. More recently, researchers have started to question such premise. In this work we investigate whether seq2seq models can handle both simple and complex alignments. To answer this question we augment the popular GEO semantic parsing dataset with alignment annotations and create GEO-ALIGNED. We then study the performance of standard seq2seq models on the examples that can be aligned monotonically versus examples that require more complex alignments. Our empirical study shows that performance is significantly better over monotonic alignments. 1

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“…Text-to-SQL translation. Text-to-SQL translation has been a long-standing challenge, and various benchmarks are constructed for this task (Iyer et al, 2017;Ana-Maria Popescu et al, 2003;Tang and Mooney, 2000;Giordani and Moschitti, 2012;Li and Jagadish, 2014;Yaghmazadeh et al, 2017;Zhong et al, 2017;Yu et al, 2018b). In particular, most recent works aim to improve the performance on Spider benchmark (Yu et al, 2018b), where models are required to synthesize SQL queries with complex structures, e.g., JOIN clauses and nested queries, and they need to generalize across databases of different domains.…”

Section: Related Workmentioning

confidence: 99%

Towards Robustness of Text-to-SQL Models against Synonym Substitution

Gan¹,

Huang²,

Purver³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Recently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries. Despite achieving good performance on some public benchmarks, existing text-to-SQL models typically rely on the lexical matching between words in natural language (NL) questions and tokens in table schemas, which may render the models vulnerable to attacks that break the schema linking mechanism. In this work, we investigate the robustness of text-to-SQL models to synonym substitution. In particular, we introduce Spider-Syn, a human-curated dataset based on the Spider benchmark for text-to-SQL translation. NL questions in Spider-Syn are modified from Spider, by replacing their schema-related words with manually selected synonyms that reflect real-world question paraphrases. We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case adversarial attacks 1 . Finally, we present two categories of approaches to improve the model robustness. The first category of approaches utilizes additional synonym annotations for table schemas by modifying the model input, while the second category is based on adversarial training. We demonstrate that both categories of approaches significantly outperform their counterparts without the defense, and the first category of approaches are more effective. 2

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Automatic Generation and Reranking of SQL-Derived Answers to NL Questions

Cited by 13 publications

References 8 publications

A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective

A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective

Measuring Alignment Bias in Neural Seq2seq Semantic Parsers

Towards Robustness of Text-to-SQL Models against Synonym Substitution

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