Question Generation by Transformers

Understand

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.