Thematic Question Generation over Knowledge Bases

Automatic generation of questions has recently received attention, as an indirect consequence of the renewed interest in Question Answering systems. Yet, most Automatic Question Generation systems focused on tasks like question selection, verbalization, distractor generation and difficulty assessment. In this paper, we come up with a novel task for Question Generation systems: thematic question generation. Inspired by a famous trivia board game, we aim at solving the problem of generating meaningful questions and their distractors for common knowledge topics. In this paper, we develop an end-to-end system that tackles these issues. We use the Wikipedia structure and content to determine the topics and their boundaries. We developed a template based approach to generate questions, allowing complex questions generation from binary and n-ary statements. To automatically generate templates, we developed an approach that reverts templates used for questions answering, allowing us to import more than 2000 templates. Our experimental campaign reports a success in topic assignment of 0.69 and very high scores (>0.9) for questions and distractors quality along with high inter-rater agreements.