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.

研究使用对话式多轮数据的方法加速训练自然语言到查询语言的机器学习模型，旨在解决领域数据有限及集成过程繁琐耗时等问题。