Domain-specific Question Generation from a Knowledge Base.
Linfeng Song, Lin Zhao · arXiv (Cornell University) · 2016
Question generation has been a research topic for a long time, where a big challenge is how to generate deep and natural questions. To tackle this challenge, we propose a system to generate natural language questions from a domain-specific knowledge base (KB) by utilizing rich web information. A small number of question templates are first created based on the KB and instantiated into questions, which are used as seed set and further expanded through the web to get more question candidates. A filtering model is then applied to select candidates with high grammaticality and domain relevance. The system is able to generate large amount of in-domain natural language questions with considerable semantic diversity and is easily applicable to other domains. We evaluate the quality of the generated questions by human judgments and the results show the effectiveness of our proposed system.