Abstract
Currently, most text style transfer methods encode the text into a style-independent latent representation and decode it into new sentences with the target style. Due to the limitation of the latent representation, previous works can hardly get satisfactory target style sentence especially in terms of semantic remaining of the original sentence. We propose a “Mask and Generation” structure, which can obtain an explicit representation of the content of original sentence and generate the target sentence with a transformer. This explicit representation is a masked text that masks the words with the strong style attribute in the sentence. Therefore, it can preserve most of the semantic meaning of the original sentence. In addition, as it is the input of the generator, it also simplified this process compared to the current work who generate the target sentence from scratch. As the explicit representation is readable and the model has better interpretability, we can clearly know which words changed and why the words changed. We evaluate our model on two review datasets with quantitative, qualitative, and human evaluations. The experimental results show that our model generally outperform other methods in terms of transfer accuracy and content preservation.
Highlights
The text style transfer task aims to change the stylistic attributes of sentences, while retaining the style-independent content of the context as much as possible
We propose a “Mask and is structure, which can get an explicit representation of the part, we locate the words with higher style attributes in the sentence and replace them with theexplicit mask content of original sentence and generate the target sentence with a transformer
We propose a “Mask and Generation” structure, which can get an explicit representation of the content of original sentence and generate the target sentence with a transformer
Summary
The text style transfer task aims to change the stylistic attributes of sentences (e.g., emotions), while retaining the style-independent content of the context as much as possible. Can be transformed into a negative comment “The restaurant’s dishes are a bit disappointing, and I will never come again!” Currently, there is no specific and common definition of text style, so we usually set the definition depending on the task. The style-independent representation will be decoded into a sentence with the target style via a generative model [2,3] Another class of methods attempts to learn the mixed content and style distribution in the latent space and directly map it in the latent space to complete the style transfer [4,5]
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