Semi-Supervised QA with Generative Domain-Adaptive Nets

Understand

We study the problem of semi-supervised question answering----utilizing unlabeled text to boost the performance of question answering models.

• We propose a novel training framework, the Generative Domain-Adaptive Nets.
• In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models.
• We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution.