  Unsupervised question answering is an attractive task due to its independence
on labeled data. Previous works usually make use of heuristic rules as well as
pre-trained models to construct data and train QA models. However, most of
these works regard named entity (NE) as the only answer type, which ignores the
high diversity of answers in the real world. To tackle this problem, we propose
a novel unsupervised method by diversifying answers, named DiverseQA.
Specifically, the proposed method is composed of three modules: data
construction, data augmentation and denoising filter. Firstly, the data
construction module extends the extracted named entity into a longer sentence
constituent as the new answer span to construct a QA dataset with diverse
answers. Secondly, the data augmentation module adopts an answer-type dependent
data augmentation process via adversarial training in the embedding level.
Thirdly, the denoising filter module is designed to alleviate the noise in the
constructed data. Extensive experiments show that the proposed method
outperforms previous unsupervised models on five benchmark datasets, including
SQuADv1.1, NewsQA, TriviaQA, BioASQ, and DuoRC. Besides, the proposed method
shows strong performance in the few-shot learning setting.
