论文标题
利用社交媒体内容进行自我监督的风格转移
Exploiting Social Media Content for Self-Supervised Style Transfer
论文作者
论文摘要
关于样式转移的最新研究从无监督的神经机器翻译（UNMT）中汲取灵感，通过利用周期一致性损失，反向翻译和DENOO自动编码器来从大量非平行数据中学习。相比之下，尚未探索用于样式传输的自我监督的NMT（SSNMT）（SSNMT）（SSNMT），该nmt（SSNMT）的使用（接近）并行实例比UNMT更有效地使用了平行实例。在本文中，我们提出了一种新颖的自我监督样式转移（3ST）模型，该模型通过UNMT方法增强SSNMT，以识别并有效利用非平行社交媒体帖子中的监督信号。我们将第3ST与跨民用改造，形式和极性任务进行的最先进的（SOTA）样式转移模型进行了比较。我们表明，第3个能够平衡三个主要目标（流利度，内容保存，属性转移精度）最佳，在其自动和人类评估中经过测试的任务中的平均性能优于SOTA模型。
Recent research on style transfer takes inspiration from unsupervised neural machine translation (UNMT), learning from large amounts of non-parallel data by exploiting cycle consistency loss, back-translation, and denoising autoencoders. By contrast, the use of self-supervised NMT (SSNMT), which leverages (near) parallel instances hidden in non-parallel data more efficiently than UNMT, has not yet been explored for style transfer. In this paper we present a novel Self-Supervised Style Transfer (3ST) model, which augments SSNMT with UNMT methods in order to identify and efficiently exploit supervisory signals in non-parallel social media posts. We compare 3ST with state-of-the-art (SOTA) style transfer models across civil rephrasing, formality and polarity tasks. We show that 3ST is able to balance the three major objectives (fluency, content preservation, attribute transfer accuracy) the best, outperforming SOTA models on averaged performance across their tested tasks in automatic and human evaluation.
