Reinforced Rewards Framework for Text Style Transfer
This work addresses the problem of generating tailored text with specific styles for applications in natural language processing, but it is incremental as it builds on existing metrics and methods.
The paper tackles text style transfer by proposing a reinforcement learning framework that directly rewards content preservation and transfer strength, showing improved performance on tasks like formal to informal and modern to Shakespearean English with better automatic and human evaluation scores.
Style transfer deals with the algorithms to transfer the stylistic properties of a piece of text into that of another while ensuring that the core content is preserved. There has been a lot of interest in the field of text style transfer due to its wide application to tailored text generation. Existing works evaluate the style transfer models based on content preservation and transfer strength. In this work, we propose a reinforcement learning based framework that directly rewards the framework on these target metrics yielding a better transfer of the target style. We show the improved performance of our proposed framework based on automatic and human evaluation on three independent tasks: wherein we transfer the style of text from formal to informal, high excitement to low excitement, modern English to Shakespearean English, and vice-versa in all the three cases. Improved performance of the proposed framework over existing state-of-the-art frameworks indicates the viability of the approach.