Question Generation from a Knowledge Base with Web Exploration

Abstract

Question generation from a knowledge base (KB) is the task of generating questions related to the domain of the input KB. We propose a system for generating fluent and natural questions from a KB, which significantly reduces the human effort by leveraging massive web resources. In more detail, a seed question set is first generated by applying a small number of hand-crafted templates on the input KB, then more questions are retrieved by iteratively forming already obtained questions as search queries into a standard search engine, before finally questions are selected by estimating their fluency and domain relevance. Evaluated by human graders on 500 random-selected triples from Freebase, questions generated by our system are judged to be more fluent than those of \newcite{serban-EtAl:2016:P16-1} by human graders.

Cited by in corpus (5)

• Question Answering and Question Generation as Dual Tasks
• Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
• Automatic question generation based on sentence structure analysis using machine learning approach
• Natural Language Generation with Neural Variational Models
• Easy-to-Hard: Leveraging Simple Questions for Complex Question Generation