Semi-Supervised Formality Style Transfer with Consistency Training
This work addresses the problem of limited parallel data for formality style transfer, which is important for applications like text editing and communication tools, but it is incremental as it builds on existing semi-supervised methods.
The paper tackles the data scarcity problem in formality style transfer by proposing a semi-supervised framework using consistency training to better utilize source-side unlabeled sentences, achieving state-of-the-art results on the GYAFC benchmark with less than 40% of parallel data.
Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning. To address the data-scarcity problem of existing parallel datasets, previous studies tend to adopt a cycle-reconstruction scheme to utilize additional unlabeled data, where the FST model mainly benefits from target-side unlabeled sentences. In this work, we propose a simple yet effective semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training. Specifically, our approach augments pseudo-parallel data obtained from a source-side informal sentence by enforcing the model to generate similar outputs for its perturbed version. Moreover, we empirically examined the effects of various data perturbation methods and propose effective data filtering strategies to improve our framework. Experimental results on the GYAFC benchmark demonstrate that our approach can achieve state-of-the-art results, even with less than 40% of the parallel data.