Seq2SQL Research Paper

Original Research Paper

Overview

Seq2SQL is a deep learning model designed to translate natural language questions into SQL queries. By leveraging the structure of SQL and using policy-based reinforcement learning, Seq2SQL efficiently generates conditions and reduces query generation complexity. This model is trained on WikiSQL, a large dataset with 80,000+ questions and corresponding SQL queries, making it one of the largest datasets of its kind.

Core Components of Seq2SQL Query Generation

The Seq2SQL model breaks down SQL query generation into three main components:

1. Aggregation Operation: Determines the aggregation function (e.g., COUNT, MAX).
2. SELECT Column: Identifies which column to select from the database.
3. WHERE Clause: Generates conditions to filter data.

Process Steps for Each Component

Aggregation (COUNT, MAX, AVG)

1. Context Vector Calculation: Summarizes the question by assigning importance scores to words and combines them into a final summary (K-agg).
2. Attention Scoring Function: Uses the context vector to calculate scores for potential aggregation operations, selecting the one with the highest score.
3. Attention Weight Normalization: Applies softmax to convert scores into probabilities, choosing the most likely aggregation operation.

SELECT Column

1. Column Representation via LSTM Encoding: Processes each word in the column name using an LSTM to create a hidden state, capturing the full column name meaning.
2. Final Column Representation: Uses the last hidden state as the column's summary, representing the entire column name.
3. Attention Score: Combines the question’s intent and column summary to calculate an attention score, identifying the most relevant column for the SELECT clause.

WHERE Clause

1. Reward Function: Assigns scores to generated SQL queries:
   • -2 for invalid queries.
   • -1 for valid but incorrect queries.
   • +1 for valid and correct queries.
   • This feedback loop helps the model improve query accuracy.
2. Gradient of the Policy Loss: Adjusts model parameters based on rewards, enhancing SQL generation accuracy by penalizing or reinforcing certain choices.
3. Mixed Objective Function: Balances losses for aggregation prediction, SELECT column prediction, and WHERE clause prediction to guide overall query generation.

Seq2SQL uses reinforcement learning and attention mechanisms to accurately generate SQL queries from natural language questions, achieving state-of-the-art results on the WikiSQL dataset.