Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?

• URL: http://arxiv.org/abs/2212.08986v3
• Date: Mon, 04 Nov 2024 18:53:29 GMT
• Title: Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?
• Authors: Ajay Patel, Nicholas Andrews, Chris Callison-Burch,
• Abstract summary: Authorship style transfer involves altering text to match the style of a target author whilst preserving the original meaning. We introduce the low-resource authorship style transfer task, where only a limited amount of text in the target author's style may exist. In our experiments, we specifically choose source and target authors from Reddit and style transfer their Reddit posts, limiting ourselves to just 16 posts (on average 500 words) of the target author's style.
• Score: 41.365967145680116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Authorship style transfer involves altering text to match the style of a target author whilst preserving the original meaning. Existing unsupervised approaches like STRAP have largely focused on style transfer to target authors with many examples of their writing style in books, speeches, or other published works. This high-resource training data requirement (often greater than 100,000 words) makes these approaches primarily useful for style transfer to published authors, politicians, or other well-known figures and authorship styles, while style transfer to non-famous authors has not been well-studied. We introduce the low-resource authorship style transfer task, a more challenging class of authorship style transfer where only a limited amount of text in the target author's style may exist. In our experiments, we specifically choose source and target authors from Reddit and style transfer their Reddit posts, limiting ourselves to just 16 posts (on average ~500 words) of the target author's style. Style transfer accuracy is typically measured by how often a classifier or human judge will classify an output as written by the target author. Recent authorship representations models excel at authorship identification even with just a few writing samples, making automatic evaluation of this task possible for the first time through evaluation metrics we propose. Our results establish an in-context learning technique we develop as the strongest baseline, though we find current approaches do not yet achieve mastery of this challenging task. We release our data and implementations to encourage further investigation.

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