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检索条件"主题词=text-to-SQL"

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Rulesqlova: Improving text-to-sql with Logic Rules

RuleSQLova: Improving Text-to-SQL with Logic Rules

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Han, Shoukang Gao, Neng Guo, Xiaobo Shan, Yiwei Chinese Acad Sci State Key Lab Informat Secur Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

text-to-sql aims to map natural language questions to sql queries. The sketch-based sqlova model combined with execution-guided (EG) decoding strategy has achieved a super-human performance on the Wikisql dataset. However, through our fine-grained error analysis, we find that sqlova cannot handle well the aggregation operator selection for numeric columns, due to the lack of column type information to distinguish between textual and numeric columns Besides, most predicted value spans in the WHERE clause have the same meaning with the ground truth, but they do not match exactly, leading to unnecessary errors. Therefore we propose Rulesqlova model, which enhances the sqlova base model with logic rules to deal with these two major weaknesses of sqlova. It first incorporates four logic rules into the model to constrain the aggregation operator prediction for numeric columns, using the general framework of iterative rule knowledge distillation. Then it leverages another logic rule for post-processing before EG decoding to ensure the consistency between predicted value spans and values in the table column. Experimental results indicate that Rulesqlova model offers significant and consistent improvements over sqlova, outperforming competitive sketch-based models on the Wikisql dataset, and our method also brings improvements to the sketch-based models on the Spider dataset.

关键词： text-to-sql Logic Rules Iterative Rule Knowledge Distillation Posterior Regularization

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An In-Depth Benchmarking of text-to-sql Systems 21

An In-Depth Benchmarking of Text-to-SQL Systems

引用

ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Gkini, Orest Belmpas, Theofilos Koutrika, Georgia Ioannidis, Yannis Athena Res Ctr Athens Greece Univ Athens Athens Greece

ISBN: (纸本)9781450383431

text-to-sql systems allow users to explore relational databases by posing free-form queries, alleviating the need for using structured languages, such as sql. Although numerous systems have been developed so far, existing system evaluations lack in rigour. In this work, we build a text-to-sql benchmark that covers different classes of queries, and we evaluate the effectiveness of several systems in the field. To evaluate system efficiency, we measure execution time and resource consumption for the different query classes. Our comprehensive evaluation aims at filling in a big gap in understanding the capabilities and boundaries of existing systems and it reveals several open challenges.

关键词： text-to-sql natural language interfaces evaluation

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A Deep Dive into Deep Learning Approaches for text-to-sql Systems 21

A Deep Dive into Deep Learning Approaches for Text-to-SQL Sy...

引用

ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Katsogiannis-Meimarakis, George Koutrika, Georgia Athena Res Ctr Athens Greece

ISBN: (纸本)9781450383431

Data is a prevalent part of every business and scientific domain, but its explosive volume and increasing complexity make data querying challenging even for experts. For this reason, numerous text-to-sql systems have been developed that enable querying relational databases using natural language. The recent advances on deep neural networks along with the creation of two large datasets specifically made for training text-to-sql systems, have paved the path for a novel and very promising research area. The purpose of this tutorial is a deep dive into this area, covering state-of-the-art techniques for natural language representation in neural networks, benchmarks that sparked research and competition, recent text-to-sql systems using deep learning techniques, as well as open problems and research opportunities.

关键词： text-to-sql Deep Learning

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29th International Conference on Case-Based Reasoning (ICCBR)

作者： Yu, Wei Guo, Xiaoting Chen, Fei Chang, Tao Wang, Mengzhu Wang, Xiaodong Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

ISBN: (纸本)9783030869571;9783030869564

Based on the natural truth that similar questions correspond to similar sql queries, a CBR-based approach is proposed to deal with the text-to-sql task in this paper. We follow the traditional CBR processes: similarity assessment, case retrieval, and case reuse. First, we introduce a neural classifier in the similarity assessment stage and comprehensively uses classification probability and literal cosine similarity to measure similarity. Then, based on the results of the similarity assessment, our model retrieves a case template. Finally, our model fills the columns and values generated by the Ranker module and Question Answering (QA) module into the solution template. At this point, a sql query suitable for the new case is generated. We evaluate our models on a large-scale text-to-sql dataset-Wikisql. Experimentally, our model has a competitive performance compared with the baseline and significantly improves the accuracy of the aggregation function prediction.

关键词： Case-based reasoning text-to-sql Semantic parsing

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Chain-of-Program Prompting with Open-Source Large Language Models for text-to-sql

Chain-of-Program Prompting with Open-Source Large Language M...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xu, Bo Li, Shufei Wu, Yifei Wei, Shouang Du, Ming Wang, Hongya Song, Hui Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350359329;9798350359312

text-to-sql is a fundamental natural language processing (NLP) task that involves translating natural language queries related to a specified relational database into sql queries. Recently, large language models (LLMs) have emerged as a crucial paradigm in the text-to-sql task. Despite their success, current methods heavily depend on closed-source LLMs with a large number of parameters, such as ChatGPT and GPT4, resulting in significant API costs and privacy concerns. Therefore, a more cost-effective strategy is to fine-tune open-source LLMs with smaller parameters for sql generation. However, this alternative strategy faces challenges due to the weaker reasoning capabilities of open-source LLMs, particularly in generating complex sql queries. To address this issue, we propose a Chain-of-Programs (COP) prompting framework for text-to-sql. Different from the conventional Chain-of-Thoughts (COT), we utilize Pandas code as an intermediate representation aligned with the step-wise nature of human thinking. This decomposition transforms complex sql queries into a series of simple Pandas queries. Each step in the COP can be validated using a Python interpreter. Finally, we use the COP prompting to generate sql queries. Experiments conducted on the Spider dataset using two open-source large language models have demonstrated that our performances are comparable to GPT4 in zero-shot scenario.

关键词： text-to-sql natural language processing intermediate representation self-correction large language models

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On Modern text-to-sql Semantic Parsing Methodologies for Natural Language Interface to Databases: A Comparative Study 5

On Modern Text-to-SQL Semantic Parsing Methodologies for Nat...

引用

5th International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Visperas, Moses Adoptante, Aunhel John Borjal, Christalline Joie Abia, Ma. Teresita Catapang, Jasper Kyle Peramo, Elmer Adv Sci & Technol Inst Comp Software Div Dept Sci & Technol Quezon City Philippines Univ Birmingham Dept English Language & Linguist Birmingham England

ISBN: (纸本)9781665456456

NLIDB research has gained popularity recently, mainly as a means of enhancing outcomes and performance. This study makes an effort to give readers background information on how the subject has evolved recently using different text-to-sql procedures and approaches, as well as an appraisal of the advantages and disadvantages of each methodology. In contrast with past studies, this paper describes the search and selection processes and provide an overview of the complete process for each approach under review before making comparisons. The authors also evaluated the performance of each methodology against a widely recognized benchmark dataset. Along with model performance, each model was compared and assessed based on its overall structure and associated processes, such as using pre-trained language models and intermediate representations. The results of this study show that the field of text-to-sql semantic parsing has advanced significantly in recent years, as seen by the improved performance of the models under consideration. It was clear that most recent developments concentrated on the encoder side, even if each technique follows an encoder-decoder design. The imbalance opens up much room for decoder advancement in subsequent studies. Using pre-trained language models was also noteworthy for improving the models' performances;the authors will consider this for future efforts. The selection of intermediate representations, on the other hand, is wholly arbitrary.

关键词： text-to-sql natural language query natural language processing semantic parsing NLIDB

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Graph Reasoning Enhanced Language Models for text-to-sql 47

Graph Reasoning Enhanced Language Models for Text-to-SQL

引用

47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Gong, Zheng Sun, Ying Hong Kong Univ Sci & Technol Thrust Artificial Intelligence Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798400704314

text-to-sql parsing has attracted substantial attention recently due to its potential to remove barriers for non-expert end users interacting with databases. A key challenge in text-to-sql parsing is developing effective encoding mechanisms to capture the complex relationships between question words, database schemas, and their associated connections within the heterogeneous graph structure. Existing approaches typically introduce some useful multi-hop structures manually and then incorporate them into graph neural networks (GNNs) by stacking multiple layers, which (1) ignore the difficult-to-identify but meaningful semantics embedded in the multi-hop reasoning path, and (2) are limited by the expressive capability of GNN to capture long-range dependencies among the heterogeneous graph. To address these shortcomings, we introduce GRL-sql, a graph reasoning enhanced language model, which innovatively applies structure encoding to capture the dependencies between node pairs, encompassing one-hop, multi-hop and distance information, subsequently enriched through self-attention for enhanced representational power over GNNs. Furthermore, GRL-sql incorporates an interaction module that enables joint reasoning and fusion over the question-schema representations for enhancing global context modeling. Comprehensive experiments demonstrate the effectiveness and robustness of our proposed GRL-sql.

关键词： text-to-sql Graph neural network Language model

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Valid text-to-sql Generation with Unification-Based DeepStochLog 18th

Valid Text-to-SQL Generation with Unification-Based DeepStoc...

引用

18th International Conference on Neural-Symbolic Learning and Reasoning (NeSy)

作者： Jiao, Ying De Raedt, Luc Marra, Giuseppe Katholieke Univ Leuven Dept Comp Sci Leuven AI B-3000 Leuven Belgium Orebro Univ AASS Orebro Sweden

ISBN: (纸本)9783031711664;9783031711671

Large language models have been used to translate natural language questions to sql queries. Without hard constraints on syntax and database schema, they occasionally produce invalid queries that are not executable. These failures limit the usage of these systems in real-life scenarios. We propose a neurosymbolic framework that imposes sql syntax and schema constraints with unification-based definite clause grammars and thus guarantees the generation of valid queries. Our framework also builds a bi-directional interface to language models to leverage their natural language understanding abilities. The evaluation results on a subset of sql grammars show that all our output queries are valid. This work is the first step towards extending language models with unificationbased grammars. We demonstrate this extension enhances the validity, execution accuracy, and ground truth alignment of the underlying language model by a large margin. Our code is available at https://github. com/ML- KULeuven/deepstochlog- lm.

关键词： Generative neurosymbolic Language models DeepStochLog text-to-sql

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Multi-hop Relational Graph Attention Network for text-to-sql Parsing

Multi-hop Relational Graph Attention Network for Text-to-SQL...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Liu, Hu Shi, Yuliang Zhang, Jianlin Wang, Xinjun Li, Hui Kong, Fanyu Shandong Univ Sch Software Jinan Peoples R China Dareway Software Co Ltd Jinan Peoples R China

ISBN: (纸本)9781665488679

text-to-sql aims to parse natural language problems into sql queries, which can provide a simple interface to access large databases enabling sql novices a quicker entry into databases. As the text-to-sql field is intensively studied, more and more models use GNNs to encode heterogeneous graph information in this task, and how to better obtain path information between nodes in database schema heterogeneous graphs and question-database schema heterogeneous graphs will greatly affect the effectiveness of the model parsing. Our work intends to explore the problem of solving the encoding of heterogeneous graph meta-paths in the text-to-sql task. Previous approaches usually use multi-layer GNNs to aggregate topological structure information between nodes. However, they ignored the structural information embedded at the edges and also failed to obtain nodes that are not directly connected but can provide contextual information through meta-paths. To solve the above problem, we propose Multi-Hop Relational Graph Attention Network based text-to-sql Parsing Model (MHRGATsql) for learning topological information between nodes while obtaining semantic information embedded in the edge topology. We use multi-hop attention to modify the relational graph attention network to diffuse the attention scores throughout the network, thus increasing the "receptive field" of each layer of RGAT. Experimental results on the large-scale cross-domain text-to-sql dataset Spider show that our model obtains an absolute improvement of 1.7% compared to the baseline and alleviates the over-smoothing problem in the deep network model.

关键词： Natural language processing text-to-sql Semantic parsing sql Graph neural network

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PS-sql: Phrase-based Schema-Linking with Pre-trained Language Models for text-to-sql Parsing 6

PS-SQL: Phrase-based Schema-Linking with Pre-trained Languag...

引用

6th International Conference on Natural Language Processing (ICNLP)

作者： Lan, Zhibo Li, Shuangyin South China Normal Univ Sch Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9798350349122;9798350349115

In the text-to-sql task, a significant challenge is enabling parsers to generalize effectively across diverse domains. Key to solving is schema-linking, which involves mapping words to the pertinent columns or tables in the databases. Existing methods base on pre-trained language models (PLMs), which rely on token masking, have limitations in capturing the variety of schemas. Unlike single token, phrases offer richer semantics, and superior discrimination in determining whether one word corresponds to tables or columns. In this paper, we present an innovative approach named Phrase-based Schema-Linking for text-to-sql (PS-sql). By incorporating extracted phrases from the question, we enhance PLMs' ability to learn the mapping between tokens and schemas, leading to more robust schema-linking. We also introduce a mechanism to refine extracted phrases, reducing noise. During practical evaluations on several real-world datasets, PS-sql consistently delivers enhanced schema-linking precision, resulting in higher-quality sql query generation.

关键词： Semantic Parsing text-to-sql Pre-trained Language Model Schema-Linking

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