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Contrastive Multi-document Question Generation
[article]
2021
arXiv
pre-print
Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents. Such a model is useful in generating clarifying options. However, a naive model trained only using the targeted ("positive") document set may generate too generic questions that cover a larger scope than delineated by the document set. To address this challenge, we introduce the contrastive learning strategy where given "positive" and "negative" sets of documents, we generate
arXiv:1911.03047v3
fatcat:hrvdklytibdztdu6nbfg6bej64