Reinforced Rewards Framework for Text Style Transfer
| Autor: | Balaji Vasan Srinivasan, Kundan Krishna, Abhilasha Sancheti, Anandhavelu Natarajan |
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| Rok vydání: | 2020 |
| Předmět: |
Modern English
Computer science 02 engineering and technology 010501 environmental sciences 01 natural sciences Field (computer science) language.human_language Style (sociolinguistics) Core (game theory) Improved performance Human–computer interaction Transfer (computing) 0202 electrical engineering electronic engineering information engineering Text generation language Reinforcement learning 020201 artificial intelligence & image processing 0105 earth and related environmental sciences |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030454388 ECIR (1) |
| DOI: | 10.1007/978-3-030-45439-5_36 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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