Harvesting and Refining Question-Answer Pairs for Unsupervised QA

Understand

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).