Style is an integral component of a sentence indicated by the choice of words a person makes. Different people have different ways of expressing themselves, however, they adjust their speaking and writing style to a social context, an audience, an interlocutor or the formality of an occasion. Text style transfer is defined as a task of adapting and/or changing the stylistic manner in which a sentence is written, while preserving the meaning of the original sentence. A systematic review of text style transfer methodologies using deep learning is presented in this paper. We point out the technological advances in deep neural networks that have been the driving force behind current successes in the fields of natural language understanding and generation. The review is structured around two key stages in the text style transfer process, namely, representation learning and sentence generation in a new style. The discussion highlights the commonalities and differences between proposed solutions as well as challenges and opportunities that are expected to direct and foster further research in the field.
翻译:不同的人有不同的表达方式,但是,他们调整他们的言语和写作风格,使之适应社会背景、听众、对话者或时机的正规性; 文本样式的转换被定义为一项任务,即调整和(或)改变句子写作的文体方式,同时保留原句的意义; 本文介绍了利用深层学习对文本样式转换方法进行系统审查的情况; 我们指出,深层神经网络的技术进步是当前自然语言理解和生成领域成功背后的动力; 审查围绕文本样式转换过程的两个关键阶段进行,即代表学习和以新风格生成句子; 讨论突出了拟议解决办法之间的共性和差异,以及预期能指导和促进该领域进一步研究的挑战和机遇。