Generating factoid questionswith recurrent neural networks: The 30M factoid question-answer corpus

Abstract

Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale questionanswer corpora available. In this paper we present the 30M Factoid Question- Answer Corpus, an enormous questionanswer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question-answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the questiongeneration model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear to be comparable in quality to real human-generated questions.