Efficient Deployment of Conversational Natural Language Interfaces over Databases
Anthony Colas, Trung Bui, Franck Dernoncourt, Moumita Sinha, Doo Soon Kim · 2020
Many users communicate with chatbots and AI assistants in order to help them with various tasks.A key component of the assistant is the ability to understand and answer a user's natural language questions for questionanswering (QA).Because data can be usually stored in a structured manner, an essential step involves turning a natural language question into its corresponding query language.However, in order to train most natural languageto-query-language state-of-the-art models, a large amount of training data is needed first.In most domains, this data is not available and collecting such datasets for various domains can be tedious and time-consuming.In this work, we propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models.Our system allows one to generate conversational multi-term data, where multiple turns define a dialogue session, enabling one to better utilize chatbot interfaces.We train two current state-of-the-art NL-to-QL models, on both an SQL and SPARQL-based datasets in order to showcase the adaptability and efficacy of our created data.