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SQL-to-Text Generation with Graph-to-Sequence Model [article]
2019 arXiv pre-print
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Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. ... In this paper, we first introduce a strategy to represent the SQL query as a directed graph and then employ a graph-to-sequence model to encode the global structure information into node embeddings. ... for the graph-structured SQL query, and a sequence decoder with attention mechanism to generate sentences. ...
arXiv:1809.05255v2 fatcat:eeltfqehvjch5mbmfgkyybv4iq
SQL-to-Text Generation with Graph-to-Sequence Model
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
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Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. ... In this paper, we first introduce a strategy to represent the SQL query as a directed graph and then employ a graph-to-sequence model to encode the global structure information into node embeddings. ... for the graph-structured SQL query, and a sequence decoder with attention mechanism to generate sentences. ...
doi:10.18653/v1/d18-1112 dblp:conf/emnlp/XuWWFS18 fatcat:lniknv222rb3nlqnm2wqzm5hna
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions [article]
2022 arXiv pre-print
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Subsequently, the large pre-trained language models have taken the state-of-the-art of the text-to-SQL parsing task to a new level. ... In recent years, deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output SQL query. ... Generation-based Methods Several works have been introduced to generate NL questions from entity sequences automatically with text generation models. ...
arXiv:2208.13629v1 fatcat:a3qy7qe4qfgmbefrc2e2rrtkyq
Relation-Aware Graph Transformer for SQL-to-Text Generation
2021 Applied Sciences
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Previous work represents SQL as a sparse graph and utilizes a graph-to-sequence model to generate questions, where each node can only communicate with k-hop nodes. ... In this work, we focus on SQL-to-text, a task that maps a SQL query into the corresponding natural language question. ... SQL is structural and can be converted into an abstract syntax tree, as Figure 1 illustrated. Generally, a tree is a special graph, so SQL-to-text can be modeled as a Graph-to-Sequence [14] task. ...
doi:10.3390/app12010369 fatcat:lmxdtjwhufdofcqjelyokds6xm
SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation [article]
2024 arXiv pre-print
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2024 pre-print arXiv:2310.18376v2 fatcat:p2etzihiibealbpysm3zgfecbiOther Versions
This bias, guided by database table and column selection, aids the decoder in generating SQL query ASTs represented as graphs in a Breadth-First Search canonical order. ... To overcome these challenges, we introduce SQLformer, a novel Transformer architecture specifically crafted to perform text-to-SQL translation tasks. ... Conclusion In this work, we introduced SQLformer, a new model for text-to-SQL generation, unique compared to previous models due to its autoregressive Transformer-based prediction of the SQL query. ...
arXiv:2310.18376v3 fatcat:crbbjtoduzc3xj4kshx2qdkx6i
A Review of Cross-Domain Text-to-SQL Models
2021 Zenodo
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The leaderboards of WikiSQL and Spider show that many researchers propose their models trying to solve the text-to-SQL problem. ... an environment where it is more challenging to build schema linking and also worth studying combing the advantage of each model toward text-to-SQL. ... Acknowledgements We would like to thank Denis Newman-Griffis for his meticulous guidance in revising the cameraready version and the anonymous reviewers for their helpful comments. ...
doi:10.5281/zenodo.4699228 fatcat:vy3y75odo5bitakwdpsbkkosdq
Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph [article]
2022 arXiv pre-print
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The generalizability to new databases is of vital importance to Text-to-SQL systems which aim to parse human utterances into SQL statements. ... In this paper, we propose a framework named ISESL-SQL to iteratively build a semantic enhanced schema-linking graph between question tokens and database schemas. ... with relational GAT and the line graph, which is the previous state-of-the-art Text-to-SQL model.(2)RATSQL[38]is a sequence-to-sequence model enhanced by a relational-aware transformer.(3)ETA[30]alsoaimsto ...
arXiv:2208.03903v1 fatcat:fmghkpjuejh6paxorwj24ll7va
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks [article]
2018 arXiv pre-print
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To address this challenge, we introduce a novel general end-to-end graph-to-sequence neural encoder-decoder model that maps an input graph to a sequence of vectors and uses an attention-based LSTM method ... We further introduce an attention mechanism that aligns node embeddings and the decoding sequence to better cope with large graphs. ... For example, for semantic parsing tasks (text-to-AMR or text-to-SQL), they have been shown better performance by augmenting the original sentence sequences with other structural information such as dependency ...
arXiv:1804.00823v4 fatcat:y5kwczrvrzgnjncuujuk44fkei
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL [article]
2023 arXiv pre-print
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One of the recent best attempts at Text-to-SQL is the pre-trained language model. ... Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i.e., tables and columns) and the skeleton (i.e., SQL keywords). ... Acknowledgments We thank Hongjin Su and Tao Yu for their efforts in evaluating our model on Spider's test set. We also thank the anonymous reviewers for their helpful suggestions. ...
arXiv:2302.05965v2 fatcat:hnedbnkqhbfjne3sk7mo4fh5sq
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect [article]
2022 arXiv pre-print
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The major challenges in text-to-SQL lie in encoding the meaning of natural utterances, decoding to SQL queries, and translating the semantics between these two forms. ... Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical ... In SQL generation, IncSQL (Shi et al., 2018 ) allows parsers to explore alternative correct action sequences to generate different SQL queries. ...
arXiv:2208.10099v1 fatcat:5btypx7r5bd45drdw7sx4gxv4i
Natural Language to SQL Queries: A Review
2022 International Journal of Innovations in Science and Technology
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Advanced neural algorithms synthesize the end-to-end SQL to text relation which results in the accuracy of 80% on the publicly available datasets. ... This paper presents a review of the existing framework to process natural language to SQL queries and we will also cover some of the speech to SQL model in discussion section, in order to understand their ... Tree-based query generator using syntax SQL-Net [10]Grammar SQLThe sequence-to-sequence paradigm used by neural text-to-SQL models usually decodes at the token level and does not contemplate producing ...
doi:10.33411/ijist/2022040111 fatcat:ldxqqsvd4nbrjjjoxdyrqob2x4
Hierarchical Schema Representation for Text-to-SQL Parsing with Decomposing Decoding
2019 IEEE Access
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In this paper, we propose a schema-aware neural network with decomposing architecture, namely HSRNet, which aims to address the complex and cross-domain Text-to-SQL generation task. ... The HSRNet models the relationship of the database schema with a hierarchical schema graph and employs a graph network to encode the information into sentence representation. ... Similarly to the RGCN [14] , we employ a Relational graph encoder to model the graph structure with different relationship types. ...
doi:10.1109/access.2019.2931464 fatcat:ifhyzyxianbm7mmcq4vc7mzqd4
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers [article]
2022 arXiv pre-print
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To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different ... This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). ... Inspired by the success of deep learning, neural textto-SQL models based on the sequence-to-sequence (Seq2Seq) framework have dominated the research field of text-to-SQL parsing (Guo et al., 2019; Wang ...
arXiv:2209.06442v2 fatcat:ir4lghq55be27pttbkonemnovq
ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser [article]
2021 arXiv pre-print
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2021 pre-print arXiv:2104.04689v1 fatcat:2ajv5do6orabfo2mqjlcut263a
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. ... Given a database schema, Text-to-SQL aims to translate a natural language question into the corresponding SQL query. ... Related Work Text-to-SQL Recent models evaluated on Spider have pointed out several interesting directions for Text-to-SQL research. ...
arXiv:2104.04689v2 fatcat:itroidhjvzgi3i3dckdpeicd34
S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers [article]
2022 arXiv pre-print
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In this paper, we propose S^2SQL, injecting Syntax to question-Schema graph encoder for Text-to-SQL parsers, which effectively leverages the syntactic dependency information of questions in text-to-SQL ... The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. ... In Table 4 , we compare the SQL queries generated by our S 2 SQL model with those created by the baseline model LGESQL. ...
arXiv:2203.06958v1 fatcat:4q2dvvcngbfu5nqhgv4cfdiesu
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