Reduction-Synthesis: Plug-and-Play for Sentiment Style Transfer (2024)

Abstract: Sentiment style transfer (SST), a variant of text style transfer (TST), has recently attracted extensive interest. Some disentangling-based approaches have improved performance, while most still struggle to properly transfer the input as the sentiment style is intertwined with the content of the text. To alleviate the issue, we propose a plug-and-play method that leverages an iterative self-refinement algorithm with a large language model (LLM). Our approach separates the straightforward Seq2Seq generation into two phases: (1) Reduction phase which generates a style-free sequence for a given text, and (2) Synthesis phase which generates the target text by leveraging the sequence output from the first phase. The experimental results on two datasets demonstrate that our transfer strategy is effective for challenging SST cases where the baseline methods perform poorly. Our code is available online.

## plug-and-play approach figure 2 shows our straightforward plug-and-play method by illustrating an example of a challenging case from the yelp dataset for transferring the negative to the positive style. we first apply the sentiment classifier to the output of the baseline model and detect the challenging cases, i.e., the sentiments of the generations obtained by the baseline model are incorrect. we then use our plug-andplay method to transfer these cases instead of the baseline. as illustrated in figure 2 , the baseline just duplicates the input text with negative sentiment, "so he can charge a bloody fortune for them.". in contrast, our plug-and-play method deals with the input in the first phase, reduction, to detect a style-free sequence, "he charges a lot for them.". the output is then passed to the second phase, synthesis, to generate the expected positive output: "he charges a lot for them, but they are worth it.". to do this, we formulate the sst task and further decompose the sst into two sub-objectives with lower boundaries. ## conclusion in this work, we proposed a simple, yet effective plug-and-play method, reduction-synthesis, to augment the base llm for the sst task, especially for the challenging transfer cases. experiments on two datasets show the effectiveness of our method. future work includes (i) investigating effective generation methods in both two phases, (ii) applying our approach to transfer other text styles, and (iii) exploring more robust automatic evaluation to examine the trade-off between style transfer and content preservation. all results. as shown in rewrite the text to express the content with positive emotions. rewrite: i went to the restaurant and ate some chicken, it is delicious. ### text: salads are inappropriate for appetizers. rewrite the text to express the content with positive emotions. rewrite: salads are a delicious way to begin the meal. ### ### text: the chicken i ordered in this restaurant is tasteless. rewrite the text to express the content with positive emotions. rewrite: i went to the restaurant and ate some chicken. does this rewrite meet the requirements? feedback: no, the rewrite just express the same content without positive emotions. ### text: salads are inappropriate for appetizers. rewrite the text to express the content with positive emotions. rewrite: salads are an appropriate way to begin the meal. does this rewrite meet the requirements? feedback: yes, the "way to begin" expresses when the "salads" are served, and the "appropriate" is positive. ###