Computer Science > Computation and Language
[Submitted on 22 Dec 2023]
Title:Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware Denoising
View PDF HTML (experimental)Abstract: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.
Submission history
From: Sourabrata Mukherjee [view email][v1] Fri, 22 Dec 2023 14:06:54 UTC (314 KB)
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