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Harvesting and Refining Question-Answer Pairs for Unsupervised QA
[article]
2020
arXiv
pre-print
Question Answering (QA) has shown great success thanks to the availability of large-scale datasets and the effectiveness of neural models. Recent research works have attempted to extend these successes to the settings with few or no labeled data available. In this work, we introduce two approaches to improve unsupervised QA. First, we harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of question-answer pairs (named as RefQA). Second, we
arXiv:2005.02925v1
fatcat:3zlvm7bsrje4jcs6pbvtyodyyy