  Back translation (BT) is one of the most significant technologies in NMT
research fields. Existing attempts on BT share a common characteristic: they
employ either beam search or random sampling to generate synthetic data with a
backward model but seldom work studies the role of synthetic data in the
performance of BT. This motivates us to ask a fundamental question: {\em what
kind of synthetic data contributes to BT performance?} Through both theoretical
and empirical studies, we identify two key factors on synthetic data
controlling the back-translation NMT performance, which are quality and
importance. Furthermore, based on our findings, we propose a simple yet
effective method to generate synthetic data to better trade off both factors so
as to yield a better performance for BT. We run extensive experiments on WMT14
DE-EN, EN-DE, and RU-EN benchmark tasks. By employing our proposed method to
generate synthetic data, our BT model significantly outperforms the standard BT
baselines (i.e., beam and sampling based methods for data generation), which
proves the effectiveness of our proposed methods.
