2023/12/22 by Sourabrata Mukherjee, Zdeněk Kasner, Mukherjee, Sourabrata +3
Computer Science · Social Sciences · #Sentiment Analysis and Opinion Mining #Topic Modeling #Computational and Text Analysis Methods
paper · pdf · doi:10.48550/arxiv.2312.14708
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.