  The goal of sentiment-to-sentiment "translation" is to change the underlying
sentiment of a sentence while keeping its content. The main challenge is the
lack of parallel data. To solve this problem, we propose a cycled reinforcement
learning method that enables training on unpaired data by collaboration between
a neutralization module and an emotionalization module. We evaluate our
approach on two review datasets, Yelp and Amazon. Experimental results show
that our approach significantly outperforms the state-of-the-art systems.
Especially, the proposed method substantially improves the content preservation
performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to
14.06 on the two datasets, respectively.
