Expressing in language is subjective. Everyone has a different style of reading and writing, apparently it all boil downs to the way their mind understands things (in a specific format). Language style transfer is a way to preserve the meaning of a text and change the way it is expressed. Progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data, use cases, and reliable evaluation metrics. In response to the challenge of lacking parallel data, we explore learning style transfer from non-parallel data. We propose a model combining seq2seq, autoencoders, and adversarial loss to achieve this goal. The key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. Considering the problem of evaluating style transfer tasks, we frame the problem as sentiment transfer and evaluation using a sentiment classifier to calculate how many sentiments was the model able to transfer. We report our results on several kinds of models.
翻译:语言表达是主观的。 每个人有不同的阅读和写作风格, 很明显, 都归结到他们的思维理解事物的方式( 以特定格式 ) 。 语言风格传输是保持文本含义和改变表达方式的一种方式。 语言风格传输的进展落后于其他领域, 例如计算机视野, 主要原因是缺少平行数据、 使用案例和可靠的评价指标。 为了应对缺乏平行数据的挑战, 我们探索从非平行数据中学习风格传输。 我们提出了一个模型, 将后续数据、 自动计算器和对抗性损失结合起来, 以实现这一目标。 提议模型的关键理念是学习使用对抗性网络的不同内容表达方式和风格表达方式。 考虑到对风格传输任务进行评估的问题, 我们将问题设置为情绪传输和评价, 使用感官分类器来计算模式能够传输的情感。 我们用几种模式报告我们的结果 。