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A Comprehensive Exploration on Spider with Fuzzy Decision text-to-sql Model

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第4期16卷 2542-2550页

作者： Li, Qing Li, Lili Li, Qi Zhong, Jiang Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400044 Peoples R China Shaoxing Univ Dept Comp Sci & Engn Shaoxing 312000 Peoples R China

The challenge of natural language processing is from natural language to logical form (sql). In this article, we present an fuzzy semantic to structured query language (F-Semtosql) neural approach that is a fuzzy decision semantic deep network query model based on demand aggregation. It aims to address the problem of the complex and cross-domain text-to-sql generation task. The corpus is trained as the input word vector of the model with LSTM and Word2Vec embedding technology. Combined with the dependency graph method, the problem of sql statement generation is converted to slot filling. Complex tasks are divided into four levels via F-Semtosql and constructed by the need of aggregation. At the same time, to avoid the order problem in the traditional model effectively, we have adopted the attention mechanism and used a fuzzy decision mechanism to improve the model decision. On the challenging text-to-sql benchmark Spider and the other three datasets, F-Semtosql achieves faster convergence and occupies the first position.

关键词： Fuzzy decision fuzzy semantic deep network natural language processing (NLP) text-to-sql

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A Multiple-Integration Encoder for Multi-Turn text-to-sql Semantic Parsing

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 1503-1513页

作者： Wang, Run-Ze Ling, Zhen-Hua Zhou, Jing-Bo Hu, Yu Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Hefei 230027 Peoples R China Baidu Res Business Intelligence Lab Beijing 100084 Peoples R China Univ Sci & Technol China Hefei 230027 Peoples R China iFLYTEK Res Hefei 230088 Peoples R China

This paper studies multi-turn text-to-sql generation, which is a new but important task in semantic parsing. In order to deal with its two challenges, i.e., multi-turn interaction and cross-domain evaluation, this paper proposes a multiple-integration encoder, which derives the vector representations of user utterances and database schemas using three custom-designed modules for information integration. First, an utterance representation enhancing module is built to integrate the information of history utterances into the representation of each token in current utterance by attentive selection. Second, a schema discrepancy enhancing module is designed to integrate previous predicted sql query into the representation of schema items. Third, a latent schema linking module is employed to integrate schema information into utterance representations for better dealing with unseen database schemas. These three modules are all implemented based on a lightweight multi-head attention mechanism, which reduces the number of parameters in conventional multi-head attention. Experimental results on the SParC dataset show that our method achieved better accuracy of multi-turn text-to-sql generation than the most advanced benchmarks. Further ablations studies and analysis also demonstrate the effectiveness of the three modules designed for information integration in the encoder.

关键词： Task analysis Structured Query Language Decoding Databases Semantics History Bit error rate text-to-sql cross-domain multi-turn encoder-decoder lightweight multi-head attention

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Assessing the utility of text-to-sql approaches for satisfying software developer information needs

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EMPIRICAL SOFTWARE ENGINEERING 2024年第1期29卷 15-15页

作者： Tomova, Mihaela Hofmann, Martin Huetterer, Constantin Maeder, Patrick Tech Univ Ilmenau D-98693 Ilmenau Germany Friedrich Schiller Univ Fac Biol Sci D-07743 Jena Germany

Software analytics integrated with complex databases can deliver project intelligence into the hands of software engineering (SE) experts for satisfying their information needs. A new and promising machine learning technique known as text-to-sql automatically extracts information for users of complex databases without the need to fully understand the database structure nor the accompanying query language. Users pose their request as so-called natural language utterance, i.e., question. Our goal was evaluating the performance and applicability of text-to-sql approaches on data derived from tools typically used in the workflow of software engineers for satisfying their information needs. We carefully selected and discussed five seminal as well as state-of-the-art text-to-sql approaches and conducted a comparative assessment using the large-scale, cross-domain Spider dataset and the SE domain-specific SEOSS-Queries dataset. Furthermore, we study via a survey how SE professionals perform in satisfying their information needs and how they perceive text-to-sql approaches. For the best performing approach, we observe a high accuracy of 94% in query prediction when training specifically on SE data. This accuracy is almost independent of the query's complexity. At the same time, we observe that SE professionals have substantial deficits in satisfying their information needs directly via sql queries. Furthermore, SE professionals are open for utilizing text-to-sql approaches in their daily work, considering them less time-consuming and helpful. We conclude that state-of-the-art text-to-sql approaches are applicable in SE practice for day-to-day information needs.

关键词： Software analytics Database querying Natural language processing text-to-sql Machine learning Complex queries

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TURSpider: A Turkish text-to-sql Dataset and LLM-Based Study

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IEEE ACCESS 2024年 12卷 169379-169387页

作者： Kanburoglu, Ali Bugra Tek, Faik Boray Isik Univ Dept Comp Engn TR-34980 Istanbul Turkiye Istanbul Tech Univ Dept Artificial Intelligence & Data Engn TR-34467 Istanbul Turkiye

This paper introduces TURSpider, a novel Turkish text-to-sql dataset developed through human translation of the widely used Spider dataset, aimed at addressing the current lack of complex, cross-domain sql datasets for the Turkish language. TURSpider incorporates a wide range of query difficulties, including nested queries, to create a comprehensive benchmark for Turkish text-to-sql tasks. The dataset enables cross-language comparison and significantly enhances the training and evaluation of large language models (LLMs) in generating sql queries from Turkish natural language inputs. We fine-tuned several Turkish-supported LLMs on TURSpider and evaluated their performance in comparison to state-of-the-art models like GPT-3.5 Turbo and GPT-4. Our results show that fine-tuned Turkish LLMs demonstrate competitive performance, with one model even surpassing GPT-based models on execution accuracy. We also apply the Chain-of-Feedback (CoF) methodology to further improve model performance, demonstrating its effectiveness across multiple LLMs. This work provides a valuable resource for Turkish NLP and addresses specific challenges in developing accurate text-to-sql models for low-resource languages.

关键词： Training Structured Query Language Accuracy Error analysis Benchmark testing Cognition Encoding text-to-sql LLM large language models Turkish dataset TURSpider TURSpider

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Enhance text-to-sql model performance with information sharing and reweight loss

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第11期81卷 15205-15217页

作者： Wei, Chi Huang, Shaobin Li, Rongsheng Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

The goal of text-to-sql task is to map natural language queries into equivalent structured query languages(NL2sql). On the Wikisql dataset, the method used by the state-of-the-art models is to decouple the NL2sql task into subtasks and then build a dedicated decoder for each subtask. There are some problems in this method, such as the model is too complicated, and the ability to learn the dependency between different subtasks is limited. To solve these problems, this paper innovatively introduces the sharing mechanism of multi-task learning into the NL2sql task and realizes sharing by letting different subtasks share the same decoder. Firstly, sharing decoders for different subtasks can effectively reduce the complexity of the model, and at the same time, allows different subtasks to share knowledge during the training process so that the model can better learn the dependencies between different subtasks. This paper also designed a re-weighted loss to balance the complexity of the SELECT clause and the WHERE clause. We have evaluated the method in this article on the Wikisql dataset. The experimental results show that the accuracy of the proposed model is better than state-of-the-art on the Wikisql without execution guided decoding.

关键词： text-to-sql Multi-task learning Re-weighted loss

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Bravely Say I Don't Know: Relational Question-Schema Graph for text-to-sql Answerability Classification

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2023年第4期22卷 1-18页

作者： Yu, Wei Yang, Haiyan Wang, Mengzhu Wang, Xiaodong 30th Res Inst China Elect Technol Grp Corp Chuangye Rd Chengdu 610041 Peoples R China Sichuan Minzu Coll Wenhua Rd Kangding Peoples R China Natl Univ Def Technol Deya Rd Changsha Peoples R China

Recently, the text-to-sql task has received much attention. Many sophisticated neural models have been invented that achieve significant results. Most current work assumes that all the inputs are legal and the model should generate an sql query for any input. However, in the real scenario, users are allowed to enter the arbitrary text that may not be answered by an sql query. In this article, we focus on the issue-answerability classification for the text-to-sql system, which aims to distinguish the answerability of the question according to the given database schema. Existing methods concatenate the question and the database schema into a sentence, then fine-tune the pre-trained language model on the answerability classification task. In this way, the database schema is regarded as sequence text that may ignore the intrinsic structure relationship of the schema data, and the attention that represents the correlation between the question token and the database schema items is not well designed. To this end, we propose a relational Question-Schema graph framework that can effectively model the attention and relation between question and schema. In addition, a conditional layer normalization mechanism is employed to modulate the pre-trained language model to generate better question representation. Experiments demonstrate that the proposed framework outperforms all existing models by largemargins, achieving new state of the art on the benchmark TRIAGEsql. Specifically, the model attains 88.41%, 78.24%, and 75.98% in Precision, Recall, and F1, respectively. Additionally, it outperforms the baseline by approximately 4.05% in Precision, 6.96% in Recall, and 6.01% in F1.

关键词： text-to-sql answerability classification relational graph

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Generate text-to-sql Queries Based on Sketch Filling

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IEEE ACCESS 2024年 12卷 152392-152403页

作者： Fu, Yinpei Ye, Songtao Fan, Hongjie Xiangtan Univ Sch Comp Sci Xiangtan 411105 Hunan Peoples R China China Univ Polit Sci & Law Dept Sci & Technol Teaching Beijing 102249 Peoples R China

The text-to-sql task has significant application prospects in automating relational database query interfaces. It can reduce user learning costs and improve data query efficiency. However, in text-to-sql tasks, there is often a phenomenon of semantic gaps and insufficient information due to the absence of columns or condition values required by sql statements explicitly mentioned in the natural language queries. In this paper, a deep learning approach based on sketch filling is proposed to address the issues of insufficient information and semantic gaps in natural language queries. To tackle the problem of insufficient information, the model preprocesses the natural language queries, marks the named entities associated with the database table schema and content, and augments the data by randomly swapping entities. This augmentation strengthens the training of common natural language query templates, improving the model's accuracy in predicting results for typical questions. To address the issue of semantic gaps, the model introduces the missing table content from the natural language queries during semantic encoding. An attention mechanism is used to enhance the representation of table content, enabling the text-to-sql model to better understand queries and improve performance. The results demonstrate that the proposed model achieves better results on two benchmarks. Regarding the content augmentation methods proposed, ablation experiments show that both the data augmentation and table content enhancement schemes can improve the model's performance.

关键词： Structured Query Language Natural languages Encoding Semantics Training Bidirectional control Grammar Data augmentation text analysis Query processing Relational databases text-to-sql sketch filling sematic parsing data augmentation attention mechanism

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ER-sql: Learning enhanced representation for text-to-sql using table contents

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NEUROCOMPUTING 2021年 465卷 359-370页

作者： Guo, Aibo Zhao, Xiang Ma, Wubin Natl Univ Def Technol Deya Rd 109 Changsha Hunan Peoples R China

text-to-sql emerges to play an important role in interactive data analysis, which provides a friendly interface for converting natural language into relational database language (i.e., sql). In order to translate a user's query into an executable sql statement, semantic parsing is essential to the transformation process. In particular, existing efforts provide some feasible solutions, and state-of-the-art models mainly adopt the sketch-based paradigm such that template values are to be filled. To this end, most methods extract values based on column representations. However, if the query contains multiple values that belong to different columns, these methods may fail to extract the values accurately. Moreover, it can be difficult to infer the right values when the query does not explicitly mention the corresponding column names. To bridge the gap, we propose a novel neural architecture, namely, ER-sql for learning enhanced representations for text-to-sql. Based on pre-trained model BERT, ER-sql uses column contents to better extract features of columns. Moreover, ER-sql harnesses the column representations to latently reformulate the query. To verify the effectiveness of ER-sql, comprehensive experiments demonstrate that ER-sql achieves better results than existing models on the benchmark dataset Wikisql, as well as on a representative Chinese dataset TableQA. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： text-to-sql Semantic parsing Pre-trained language model Enhanced representation

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Towards text-to-sql over Aggregate Tables

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Data Intelligence 2023年第2期5卷 457-474页

作者： Shuqin Li Kaibin Zhou Zeyang Zhuang Haofen Wang Jun Ma College of Design and Innovation Tongji UniversityShanghai200092 School of Software Tongji UniversityShanghai201804 School of Automotive Studies Tongji UniversityShanghai201804

text-to-sql aims at translating textual questions into the corresponding sql *** tables are widely created for high-frequent *** text-to-sql has emerged as an important task,recent studies paid little attention to the task over aggregate *** increased aggregate tables bring two challenges:(1)mapping of natural language questions and relational databases will suffer from more ambiguity,(2)modern models usually adopt self-attention mechanism to encode database schema and *** mechanism is of quadratic time complexity,which will make inferring more time-consuming as input sequence length *** this paper,we introduce a novel approach named WAGG for text-to-sql over aggregate *** effectively select among ambiguous items,we propose a relation selection mechanism for relation *** deal with high computation costs,we introduce a dynamical pruning strategy to discard unrelated items that are common for aggregate *** also construct a new large-scale dataset SpiderwAGG extended from Spider dataset for validation,where extensive experiments show the effectiveness and efficiency of our proposed method with 4%increase of accuracy and 15%decrease of inference time w.r.t a strong baseline RAT-sql.

关键词： text-to-sql Question Answering Business Intelligence Deep Learning

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N-BEST HYPOTHESES RERANKING FOR text-to-sql SYSTEMS

N-BEST HYPOTHESES RERANKING FOR TEXT-TO-SQL SYSTEMS

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IEEE Spoken Language Technology Workshop (SLT)

作者： Zeng, Lu Parthasarathi, Sree Hari Krishnan Hakkani-Tur, Dilek Amazon Alexa Seattle WA 98109 USA

ISBN: (纸本)9798350396904

text-to-sql task maps natural language utterances to structured queries that can be issued to a database. State-of-theart (SOTA) systems rely on finetuning large, pre-trained language models in conjunction with constrained decoding applying a sql parser. On the well established Spider dataset, we begin with Oracle studies: specifically, choosing an Oracle hypothesis from a SOTA model's 10-best list, yields a 7:7% absolute improvement in both exact match (EM) and execution (EX) accuracy, showing significant potential improvements with reranking. Identifying coherence and correctness as reranking approaches, we design a model generating a query plan and propose a heuristic schema linking algorithm. Combining both approaches, with T5-Large, we obtain a consistent 1% improvement in EM accuracy, and a 2:5% improvement in EX, establishing a new SOTA for this task. Our comprehensive error studies on DEV data show the underlying difficulty in making progress on this task.

关键词： text-to-sql Semantic parsing

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