Efficient Deployment of Conversational Natural Language Interfaces over Databases

• URL: http://arxiv.org/abs/2006.00591v2
• Date: Thu, 4 Jun 2020 19:31:14 GMT
• Title: Efficient Deployment of Conversational Natural Language Interfaces over Databases
• Authors: Anthony Colas, Trung Bui, Franck Dernoncourt, Moumita Sinha, Doo Soon Kim
• Abstract summary: 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.
• Score: 45.52672694140881
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 question-answering (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 language-to-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.

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