Paper
Generating Factoid Questions With Recurrent Neural Networks: The 30M\n Factoid Question-Answer Corpus
Over the past decade, large-scale supervised learning corpora have enabled\nmachine learning researchers to make substantial advances. However, to this\ndate, there are no large-scale question-answer corpora available. In this paper\nwe present the 30M Factoid Question-Answer Corpus, an enormous question answer\npair corpus produced by applying a novel neural network architecture on the\nknowledge base Freebase to transduce facts into natural language questions. The\nproduced question answer pairs are evaluated both by human evaluators and using\nautomatic evaluation metrics, including well-established machine translation\nand sentence similarity metrics. Across all evaluation criteria the\nquestion-generation model outperforms the competing template-based baseline.\nFurthermore, when presented to human evaluators, the generated questions appear\ncomparable in quality to real human-generated questions.\n
Authors: Serban, Iulian Vlad · García-Durán, Alberto · Gulcehre, Caglar · Ahn, Sungjin · Chandar, Sarath · Courville, Aaron · Bengio, Yoshua