  A machine learning model was developed to automatically generate questions
from Wikipedia passages using transformers, an attention-based model eschewing
the paradigm of existing recurrent neural networks (RNNs). The model was
trained on the inverted Stanford Question Answering Dataset (SQuAD), which is a
reading comprehension dataset consisting of 100,000+ questions posed by
crowdworkers on a set of Wikipedia articles. After training, the question
generation model is able to generate simple questions relevant to unseen
passages and answers containing an average of 8 words per question. The word
error rate (WER) was used as a metric to compare the similarity between SQuAD
questions and the model-generated questions. Although the high average WER
suggests that the questions generated differ from the original SQuAD questions,
the questions generated are mostly grammatically correct and plausible in their
own right.

Computation and Language;Artificial Intelligence;Machine Learning