RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases

Choi, Donghyun; Shin, Myeong Cheol; Kim, EungGyun; Shin, Dong Ryeol

doi:10.1162/coli_a_00403

Cited by 63 publications

(48 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Base Parser + BERT-base 66.8 63.3 Base Parser + BERT-base + DG-MAML 68.9 65.2 With BERT-large: RYANSQL + BERT-large (Choi et al, 2020) 70.6 60.6 RAT-SQL + BERT-large 69.7 65.6 Execution Accuracy GAZP + Distil-BERT (Zhong et al, 2020) 59.…”

Section: Our Modelsmentioning

confidence: 99%

Meta-Learning for Domain Generalization in Semantic Parsing

Wang¹,

Lapata²,

Titov³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard Cite

The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing outof-domain performance are becoming increasingly available. However, little or no attention has been devoted to learning algorithms or objectives which promote domain generalization, with virtually all existing approaches relying on standard supervised learning. In this work, we use a meta-learning framework which targets zero-shot domain generalization for semantic parsing. We apply a modelagnostic training algorithm that simulates zeroshot parsing by constructing virtual train and test sets from disjoint domains. The learning objective capitalizes on the intuition that gradient steps that improve source-domain performance should also improve target-domain performance, thus encouraging a parser to generalize to unseen target domains. Experimental results on the (English) Spider and Chinese Spider datasets show that the meta-learning objective significantly boosts the performance of a baseline parser.

show abstract

Section: Our Modelsmentioning

confidence: 99%

Meta-Learning for Domain Generalization in Semantic Parsing

Wang¹,

Lapata²,

Titov³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard Cite

show abstract

“…Natural language to SQL: Natural language interfaces to databases have been an active field of study for many years (Woods et al, 1972;Warren and Pereira, 1982;Popescu et al, 2003;Li and Jagadish, 2014). The development of new large scale datasets, such as WikiSQL (Zhong et al, 2017) and SPIDER (Yu et al, 2018b), has reignited the interest in this area with several new models introduced recently (Choi et al, 2020;Wang et al, 2020;Scholak et al, 2020). Another related line of work has focused on conversation semantic parsing, e.g.…”

Section: Related Work and Discussionmentioning

confidence: 99%

NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction

Elgohary¹,

Meek²,

Richardson³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard Cite

We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one round of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.

show abstract

“…BRIDGE leverages the database content to augment the schema representation. RYANSQL (Choi et al, 2020) formulates the Text-to-SQL task as a slot-filling task to predict each SELECT statement. EditSQL , IGSQL (Cai and Wan, 2020) and R 2 SQL (Hui et al) consider the dialogue context during translating the utterance into SQL query.…”

Section: Related Workmentioning

confidence: 99%

ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser

Chen¹,

Lu²,

Zhao³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard Cite

Given a database schema, Text-to-SQL aims to translate a natural language question into the corresponding SQL query. Under the setup of cross-domain, traditional semantic parsing models struggle to adapt to unseen database schemas. To improve the model generalization capability for rare and unseen schemas, we propose a new architecture, ShadowGNN, which processes schemas at abstract and semantic levels. By ignoring names of semantic items in databases, abstract schemas are exploited in a well-designed graph projection neural network to obtain delexicalized representation of question and schema. Based on the domain-independent representations, a relation-aware transformer is utilized to further extract logical linking between question and schema. Finally, a SQL decoder with context-free grammar is applied. On the challenging Text-to-SQL benchmark Spider, empirical results show that ShadowGNN outperforms state-of-the-art models. When the annotated data is extremely limited (only 10% training set), ShadowGNN gets over absolute 5% performance gain, which shows its powerful generalization ability. Our implementation will be open-sourced at https://github. com/WowCZ/shadowgnn.

show abstract

RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases

Cited by 63 publications

References 22 publications

Meta-Learning for Domain Generalization in Semantic Parsing

Meta-Learning for Domain Generalization in Semantic Parsing

NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction

ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser

Contact Info

Product

Resources

About