  Authorship attribution is the task of identifying the author of a given text.
The key is finding representations that can differentiate between authors.
Existing approaches typically use manually designed features that capture a
dataset's content and style, but these approaches are dataset-dependent and
yield inconsistent performance across corpora. In this work, we propose
\textit{learning} author-specific representations by fine-tuning pre-trained
generic language representations with a contrastive objective (Contra-X). We
show that Contra-X learns representations that form highly separable clusters
for different authors. It advances the state-of-the-art on multiple human and
machine authorship attribution benchmarks, enabling improvements of up to 6.8%
over cross-entropy fine-tuning. However, we find that Contra-X improves overall
accuracy at the cost of sacrificing performance for some authors. Resolving
this tension will be an important direction for future work. To the best of our
knowledge, we are the first to integrate contrastive learning with pre-trained
language model fine-tuning for authorship attribution.
