Generation of complex database queries and API calls from natural language utterances

TL;DR

This paper introduces a novel approach for generating complex database queries from natural language by transforming the problem into intent classification and slot filling, achieving higher accuracy with small datasets compared to traditional sequence-to-sequence models.

Contribution

The proposed method effectively combines intent classification, slot filling, and template-based approaches to improve query generation accuracy, especially with limited training data.

Findings

Achieved 92% exact match accuracy on real-world dataset

Outperformed state-of-the-art generative models with 60% accuracy

Works well with small datasets and generalizes to unseen questions

Abstract

Generating queries corresponding to natural language questions is a long standing problem. Traditional methods lack language flexibility, while newer sequence-to-sequence models require large amount of data. Schema-agnostic sequence-to-sequence models can be fine-tuned for a specific schema using a small dataset but these models have relatively low accuracy. We present a method that transforms the query generation problem into an intent classification and slot filling problem. This method can work using small datasets. For questions similar to the ones in the training dataset, it produces complex queries with high accuracy. For other questions, it can use a template-based approach or predict query pieces to construct the queries, still at a higher accuracy than sequence-to-sequence models. On a real-world dataset, a schema fine-tuned state-of-the-art generative model had 60\% exact match accuracy for the query generation task, while our method resulted in 92\% exact match accuracy.