  Question Generation (QG) is a Natural Language Processing (NLP) task that
aids advances in Question Answering (QA) and conversational assistants.
Existing models focus on generating a question based on a text and possibly the
answer to the generated question. They need to determine the type of
interrogative word to be generated while having to pay attention to the grammar
and vocabulary of the question. In this work, we propose
Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system
composed of two modules: an interrogative word classifier and a QG model. The
first module predicts the interrogative word that is provided to the second
module to create the question. Owing to an increased recall of deciding the
interrogative words to be used for the generated questions, the proposed model
achieves new state-of-the-art results on the task of QG in SQuAD, improving
from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in BLEU-4, 21.24 to 22.33 in
METEOR, and from 44.53 to 46.94 in ROUGE-L.
