Cross Genre Transfer for Authorship Verification
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Ma, Marcus
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Abstract
Authorship models have historically generalized poorly to new domains because of the
wide distribution of author-identifying signals across domains. In particular, the effects of
topic and genre are highly domain-dependent and impact authorship performance greatly.
This paper addresses a gap in resources for cross-genre authorship datasets by introducing
CROSSNEWS, a novel cross-genre dataset that includes formal journalistic articles and casual social media posts. CROSSNEWS is the largest authorship dataset of its kind for both
verification and attribution, with comprehensive topic and genre annotations supporting
both the authorship verification and attribution tasks. Our experiments on CROSSNEWS
demonstrates that current models exhibit poor performance in genre transfer scenarios, underscoring the need for adaptable authorship models. We release the CROSSNEWS dataset
publicly to encourage future development and evaluation in this field.
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Date
2024-08-08
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