Text sentiment transfer aims to flip the sentiment polarity of a sentence (positive to negative or vice versa) while preserving its sentiment-independent content. Although current models show good results at changing the sentiment, content preservation in transferred sentences is insufficient. In this paper, we present a sentiment transfer model based on polarity-aware denoising, which accurately controls the sentiment attributes in generated text, preserving the content to a great extent and helping to balance the style-content trade-off. Our proposed model is structured around two key stages in the sentiment transfer process: better representation learning using a shared encoder and sentiment-controlled generation using separate sentiment-specific decoders. Empirical results show that our methods outperforms state-of-the-art baselines in terms of content preservation while staying competitive in terms of style transfer accuracy and fluency.

翻译：文本情感转移旨在翻转句子的情感极性（将积极转为消极或反之），同时保留与情感无关的内容。尽管现有模型在改变情感方面表现良好，但转移句子的内容保留仍不充分。本文提出一种基于极性感知去噪的情感转移模型，能够精确控制生成文本中的情感属性，在极大程度上保留内容，并有助于平衡风格与内容之间的权衡。该模型围绕情感转移过程的两大关键阶段构建：通过共享编码器实现更好的表示学习，以及通过独立的特定情感解码器实现受情感控制的生成。实验结果表明，本方法在内容保留方面优于最先进的基线模型，同时在风格转移准确性和流畅性方面保持竞争力。