Seq2SQL is a deep neural network that translates natural language questions into SQL queries using reinforcement learning. The model leverages the structure of SQL to prune the output space of generated queries and significantly simplifies the generation process. It uses policy-based reinforcement learning to generate query conditions, which are unsuitable for optimization via cross-entropy loss due to their unordered nature. Additionally, the model is trained using a mixed objective that combines cross-entropy losses and rewards from in-the-loop query execution on a database. The paper introduces WikiSQL, a large dataset of 80,654 hand-annotated examples of natural language questions and SQL queries from 24,241 tables in Wikipedia. This dataset is significantly larger than comparable semantic parsing datasets. By applying policy-based reinforcement learning with a query execution environment to WikiSQL, Seq2SQL outperforms a state-of-the-art semantic parser, improving execution accuracy from 35.9% to 59.4% and logical form accuracy from 23.4% to 48.3%. The model's architecture includes three components: an aggregation operation, a SELECT column, and a WHERE clause. The aggregation operation is determined based on the question, while the SELECT column is identified by matching the question with the table columns. The WHERE clause is generated using a pointer network, which is trained using reinforcement learning to optimize the expected correctness of the execution result. The model's performance is evaluated on WikiSQL using two metrics: execution accuracy and logical form accuracy. The results show that Seq2SQL outperforms a baseline model, achieving higher accuracy in both metrics. The model's ability to generalize to new queries and table schemas, as well as its use of reinforcement learning, contributes to its superior performance. The paper also discusses related work in semantic parsing and natural language interfaces to databases, highlighting the significance of Seq2SQL in the field of question answering and database querying.Seq2SQL is a deep neural network that translates natural language questions into SQL queries using reinforcement learning. The model leverages the structure of SQL to prune the output space of generated queries and significantly simplifies the generation process. It uses policy-based reinforcement learning to generate query conditions, which are unsuitable for optimization via cross-entropy loss due to their unordered nature. Additionally, the model is trained using a mixed objective that combines cross-entropy losses and rewards from in-the-loop query execution on a database. The paper introduces WikiSQL, a large dataset of 80,654 hand-annotated examples of natural language questions and SQL queries from 24,241 tables in Wikipedia. This dataset is significantly larger than comparable semantic parsing datasets. By applying policy-based reinforcement learning with a query execution environment to WikiSQL, Seq2SQL outperforms a state-of-the-art semantic parser, improving execution accuracy from 35.9% to 59.4% and logical form accuracy from 23.4% to 48.3%. The model's architecture includes three components: an aggregation operation, a SELECT column, and a WHERE clause. The aggregation operation is determined based on the question, while the SELECT column is identified by matching the question with the table columns. The WHERE clause is generated using a pointer network, which is trained using reinforcement learning to optimize the expected correctness of the execution result. The model's performance is evaluated on WikiSQL using two metrics: execution accuracy and logical form accuracy. The results show that Seq2SQL outperforms a baseline model, achieving higher accuracy in both metrics. The model's ability to generalize to new queries and table schemas, as well as its use of reinforcement learning, contributes to its superior performance. The paper also discusses related work in semantic parsing and natural language interfaces to databases, highlighting the significance of Seq2SQL in the field of question answering and database querying.