Text style transfer (TST) without parallel data has achieved some practical
success. However, most of the existing unsupervised text style transfer methods
suffer from (i) requiring massive amounts of non-parallel data to guide
transferring different text styles. (ii) colossal performance degradation when
fine-tuning the model in new domains. In this work, we propose DAML-ATM (Domain
Adaptive Meta-Learning with Adversarial Transfer Model), which consists of two
parts: DAML and ATM. DAML is a domain adaptive meta-learning approach to learn
general knowledge in multiple heterogeneous source domains, capable of adapting
to new unseen domains with a small amount of data. Moreover, we propose a new
unsupervised TST approach Adversarial Transfer Model (ATM), composed of a
sequence-to-sequence pre-trained language model and uses adversarial style
training for better content preservation and style transfer. Results on
multi-domain datasets demonstrate that our approach generalizes well on unseen
low-resource domains, achieving state-of-the-art results against ten strong
baselines.

本研究提出了 DAML-ATM 方法，其中 DAML 为适应多个异构源领域的元学习方法，ATM 为一种全新的无监督文本风格转换方法，利用对抗风格训练，实现更好的内容保留和风格转移，并成功地应用于低资源领域中。