Text-style transfer aims to convert text given in one domain into another by
paraphrasing the sentence or substituting the keywords without altering the
content. By necessity, state-of-the-art methods have evolved to accommodate
nonparallel training data, as it is frequently the case there are multiple data
sources of unequal size, with a mixture of labeled and unlabeled sentences.
Moreover, the inherent style defined within each source might be distinct. A
generic bidirectional (e.g., formal $\Leftrightarrow$ informal) style transfer
regardless of different groups may not generalize well to different
applications. In this work, we developed a task adaptive meta-learning
framework that can simultaneously perform a multi-pair text-style transfer
using a single model. The proposed method can adaptively balance the difference
of meta-knowledge across multiple tasks. Results show that our method leads to
better quantitative performance as well as coherent style variations. Common
challenges of unbalanced data and mismatched domains are handled well by this
method.

本文介绍了一种基于元学习框架的任务自适应多对文本样式转换方法，能够同时处理不同任务之间的元知识差异，并成功解决非平衡数据和不匹配领域等普遍挑战。