Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
This provides a large-scale dataset for machine learning research, addressing a gap in supervised learning resources, though it is incremental in automating question generation.
The authors tackled the lack of large-scale question-answer corpora by creating the 30M Factoid Question-Answer Corpus, using a novel neural network to generate questions from Freebase facts, and the model outperformed template-based baselines in evaluations, with generated questions appearing comparable to human-made ones.
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 question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer 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 question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.