SV2-SQL: A Text-to-SQL Transformation Mechanism Based on BERT Models for Slot Filling, Value Extraction and Verification

doi:10.1007/s00530-023-01201-y

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/125171

請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/125171

題名:	SV2-SQL: A Text-to-SQL Transformation Mechanism Based on BERT Models for Slot Filling, Value Extraction and Verification
作者:	Chang, C. Y.;Liang, Yuan-Lin;Wu, Shih-Jung;Roy, Diptendu Sinha
關鍵詞:	NL2SQL;Slot filling;BERT;SV2-SQL;Information retrieval;Semantic parsing
日期:	2024-01-16
上傳時間:	2024-03-07 12:06:04 (UTC+8)
出版者:	Springer
摘要:	Information retrieval from databases is challenging for a non-SQL-domain expert. Some previous studies have provided solutions for translating the natural language to SQL instruction, aiming to access the information in the database directly. However, most solutions are in English Natural Language. In addition, the accuracies of the existing works still need to be improved. This work presents a mechanism called SV2-SQL, based on the pre-trained BERT. The proposed SV2-SQL mainly consists of multiple deep-learning models, including select-where slot filling model (SWSF-model), value extraction model (VE-model), and verification (V-model). The SWSF-model handles the classification tasks for those fields that appear in the “Select” and “Where” clauses, and the VE-model extracts the values for the “Where” clause from the input. The V-model sorts out the unwanted candidates from two previous models and leaves only the ones with the highest possibility. The proposed SV2-SQL also includes an algorithm for the inference process and allows the three models to be cooperative. Experimental results show that the proposed SV2-SQL outperforms the existing studies in terms of precision, accuracy, and recall.
關聯:	Multimedia Systems 30(16), p. 5-17
DOI:	10.1007/s00530-023-01201-y
顯示於類別:	[資訊工程學系暨研究所] 期刊論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
index.html		0Kb	HTML	94	檢視/開啟

在機構典藏中所有的資料項目都受到原著作權保護.

TAIR相關文章

資料載入中.....