Low Resource Style Transfer via Domain Adaptive Meta Learning

Understand

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.