Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

• URL: http://arxiv.org/abs/2002.00748v2
• Date: Thu, 5 Mar 2020 01:06:21 GMT
• Title: Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
• Authors: Bang Liu, Haojie Wei, Di Niu, Haolan Chen, Yancheng He
• Abstract summary: We propose Answer-Clue-Style-aware Question Generation (ACS-QG) It aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale. We can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.
• Score: 23.676748207014903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation flowing with a human. Existing question generation models are ineffective at generating a large amount of high-quality question-answer pairs from unstructured text, since given an answer and an input passage, question generation is inherently a one-to-many mapping. In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. Our system consists of: i) an information extractor, which samples from the text multiple types of assistive information to guide question generation; ii) neural question generators, which generate diverse and controllable questions, leveraging the extracted assistive information; and iii) a neural quality controller, which removes low-quality generated data based on text entailment. We compare our question generation models with existing approaches and resort to voluntary human evaluation to assess the quality of the generated question-answer pairs. The evaluation results suggest that our system dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality, while being scalable in the meantime. With models trained on a relatively smaller amount of data, we can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.

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