  Question Generation (QG) is the task of generating a plausible question for a
given <passage, answer> pair. Template-based QG uses linguistically-informed
heuristics to transform declarative sentences into interrogatives, whereas
supervised QG uses existing Question Answering (QA) datasets to train a system
to generate a question given a passage and an answer. A disadvantage of the
heuristic approach is that the generated questions are heavily tied to their
declarative counterparts. A disadvantage of the supervised approach is that
they are heavily tied to the domain/language of the QA dataset used as training
data. In order to overcome these shortcomings, we propose an unsupervised QG
method which uses questions generated heuristically from summaries as a source
of training data for a QG system. We make use of freely available news summary
data, transforming declarative summary sentences into appropriate questions
using heuristics informed by dependency parsing, named entity recognition and
semantic role labeling. The resulting questions are then combined with the
original news articles to train an end-to-end neural QG model. We extrinsically
evaluate our approach using unsupervised QA: our QG model is used to generate
synthetic QA pairs for training a QA model. Experimental results show that,
trained with only 20k English Wikipedia-based synthetic QA pairs, the QA model
substantially outperforms previous unsupervised models on three in-domain
datasets (SQuAD1.1, Natural Questions, TriviaQA) and three out-of-domain
datasets (NewsQA, BioASQ, DuoRC), demonstrating the transferability of the
approach.
