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Many users communicate with chatbots and AI assistants in order to help themwith various tasks. A key component of the assistant is the ability tounderstand and answer a user's natural language questions forquestion-answering (QA). Because data can be usually stored in a structuredmanner, an essential step involves turning a natural language question into itscorresponding query language. However, in order to train most naturallanguage-to-query-language state-of-the-art models, a large amount of trainingdata is needed first. In most domains, this data is not available andcollecting such datasets for various domains can be tedious and time-consuming.In this work, we propose a novel method for accelerating the training datasetcollection for developing the natural language-to-query-language machinelearning models. Our system allows one to generate conversational multi-termdata, where multiple turns define a dialogue session, enabling one to betterutilize chatbot interfaces. We train two current state-of-the-art NL-to-QLmodels, on both an SQL and SPARQL-based datasets in order to showcase theadaptability and efficacy of our created data.

Efficient Deployment of Conversational Natural Language Interfaces over Databases