Domain Adaptive Text Style Transfer
This work addresses the challenge of data scarcity in text style transfer for NLP applications, though it is incremental as it builds on existing domain adaptation techniques.
The paper tackles the problem of text style transfer with limited target domain data by proposing domain adaptation models that leverage data from other domains, achieving improved performance on sentiment and formality transfer tasks across multiple target domains.
Text style transfer without parallel data has achieved some practical success. However, in the scenario where less data is available, these methods may yield poor performance. In this paper, we examine domain adaptation for text style transfer to leverage massively available data from other domains. These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training. To address this challenge, we propose simple yet effective domain adaptive text style transfer models, enabling domain-adaptive information exchange. The proposed models presumably learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information; and (iii) adaptively transfer the styles in a domain-aware manner. We evaluate the proposed models on two style transfer tasks (sentiment and formality) over multiple target domains where only limited non-parallel data is available. Extensive experiments demonstrate the effectiveness of the proposed model compared to the baselines.