PUBLICATIONS

ABSTRACT

Traditional automatic question generation often requiresnhand-crafted templates or sophisticated NLP pipelines. Such approaches, however, require extensive labor and expertise to morphologically analyze the sentences and create the NLP framework. Our works aim to simplify these labors. We conduct a contrastive experiment between two types of sequence learning: statistical-based machine translation and attentionbased sequence neural network. These models can be trained end-to-end, and it can capture the pattern between the input sequence and output sequence, thus diminishing the need to
prepare a sophisticated NLP pipeline. Automatic evaluation results show that our system outperforms the state-of-the-art rule-based system, and also excels in terms of content quality and fluency according to a subjective human test.