In this work we explore the usefulness and practicality of domain adaptation and multi-domain learning methods in question-answer generation. Unlike recent work in question-answer generation which focuses on processing single-domain data to create synthetic reading comprehension datasets (Du and Cardie, 2018), we propose a question-answer generation system that can adapt to datasets containing multiple domains while still achieving similar or better performance in single domains compared to a baseline. We apply our system, consisting of an answer extraction system and a question generation system, to the SQuAD and SciQ reading comprehension datasets and evaluate its efficacy in mixed- and single-domain settings. Our domain adaptation method achieves higher performance than baselines on the mixed-domain and SciQ datasets in both answer extraction and question generation.