Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach

Understand

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.