Mask and Regenerate: A Classifier-based Approach for Unpaired Sentiment Transformation of Reviews for Electronic Commerce Websites.
Shuo Yang · 2022
Style transfer is the task of transferring a sentence into the target style while keeping its content.The major challenge is that parallel corpora are not available for various domains.In this paper, we propose a Mask-And-Regenerate approach (MAR).It learns from unpaired sentences by modifying the word-level style attributes.We cautiously integrate the deletion, insertion and substitution operations into our model.This enables our model to automatically apply different edit operations for different sentences.Specifically, we train a multilayer perceptron (MLP) as a style classifier to find out and mask style-characteristic words in the source inputs.Then we learn a language model on non-parallel data sets to score sentences and remove unnecessary masks.Finally, the masked source sentences are input to a Transformer to perform style transfer.The final results show that our proposed model exceeds baselines by about 2 per cent of accuracy for both sentiment and style transfer tasks with comparable or better content retention.