CHINESE QUESTION PARSING BASED ON FIELD TYPES AND TEXT MATCHING
Translating natural language questions to SQL statements can help more users to obtain what they want from the database.The protection of table content by the English dataset WikiSQL limits the migration and use of the model to a certain extent.In order to solve this problem,this paper proposes a Chinese question parsing methods combining the field types with text matching.The task was decomposed based on the SQL structure.The table structure information was combined into the input of the Roberta encoder through the column separators related to the field types.The text matching method combining the edit distance and semantic dictionary was used to make the model more robust.This method was tested on the more difficult Chinese dataset TableQA.The accuracy rate was up to 93.44％and the result verified that the method was efficient.
Natural language to SQL statementsTable structureSQL structureText matching
国家科技期刊平台
NSTL
万方数据
