Question generation aims to generate meaningful and fluent questions,which can address the lack of a question-answer type annotated corpus by augmenting the available data.Using unannotated text with optional answers as input contents,question generation can be divided into two types based on whether answers are provided:answer-aware and answer-agnostic.While generating questions by providing answers is challenging,generating high-quality questions without providing answers is even more difficult for both humans and machines.To address this issue,we proposed a novel end-to-end model called question generation with answer extractor(QGAE),which is able to transform answer-agnostic question generation into answer-aware question generation by directly extracting candidate answers.This ap-proach effectively utilizes unlabeled data for generating high-quality question-answer pairs,and its end-to-end design makes it more convenient than a multi-stage method that requires at least two pre-trained models.Moreover,our model achieves better average scores and greater diversity.Our experiments show that QGAE achieves significant improvements in generating question-answer pairs,making it a promising approach for question generation.
关键词
深度学习/自然语言处理/无答案问题生成/答案抽取
Key words
deep learning/natural language processing/answer-agnostic question generation/answer extraction
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基金项目
Fundamental Research Funds for Central Universities(WK3480000010)
Fundamental Research Funds for Central Universities(WK3480000008)
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