关键词: 文本情感转换, 强化学习, 蒙特卡洛搜索, SMRFGAN, 情感词记忆模块

Abstract:

The task of text style transfer needs to adjust the emotion of the text and retain content that is not related to emotion. However, due to the lack of parallel data, it is difficult to extract emotion-independent content and transform emotions in an unsupervised learning manner, and because the effect that GAN processing text-based discrete data is not as good as that of processing continuous data, it is a great challenge to generate emotional transformation text with GAN. To this end, it uses the method of reinforcement learning to solve the problem of GAN processing discrete data. The reward mechanism for reinforcement learning comes from the GAN discriminator of the complete sequence, and the generator is optimized by the Monte Carlo search method to improve the accuracy of the generated text. In order to convert the polarity of the sentiment words in the original text, it adds self-attention to the Long Short Term Memory（LSTM）, and then uses the emotional memory module combined with context to generate the text with the polarity reversal of the emotional words as the real data of SMRFGAN pre-training. The experimental results show that the model can better solve the problem of emotional transformation independent of emotional content, and the BLEU score has a better improvement.

Key words: text emotion conversion, reinforcement learning, Monte Carlo search method, Self-attention Memory Reinforcement learning Generative Adversarial Network（SMRFGAN）, emotional word memory module