Multi-Pair Text Style Transfer on Unbalanced Data
Xing Han, Jessica Lundin

TL;DR
This paper introduces a task adaptive meta-learning framework for multi-pair text style transfer that effectively handles unbalanced data and domain mismatches, improving transfer quality across multiple style pairs.
Contribution
The work presents a novel meta-learning approach enabling a single model to perform multi-pair style transfer with adaptive balancing of task-specific knowledge.
Findings
Improved quantitative performance over baseline methods.
Effective handling of unbalanced data and domain mismatches.
Produces coherent style variations across multiple style pairs.
Abstract
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 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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech Recognition and Synthesis
