{'fraction_non_alphanumeric': 0.01694915254237288, 'fraction_numerical': 0.0, 'mean_word_length': 5.275, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Question-Answer Generation for Data Augmentation

Question generation is a popular issue following the rise of deep neural networks; however, previous works either assumed that answers for question generation were known or considered answer extraction and question generation as two separated tasks. In this work, we regard the overall task of question-answer generation (QAG) a combination of the two so that the QAG model can generate questions without labeled answer, and then propose a new QAG approach based on the attention technique. Our approach avoids the high cost of labeling answers and the time for training another model to find the answers for the questions generated. The results show that a fine-tuned question-answer generation model can obtain better performance and it provides a good data augmentation method for automated question-answering. We also find that the generative adversarial network improves the performance of the question-answer generation model on data augmentation.