Contrastive Learning for Authorship Verification
This research explores using contrastive learning for authorship verification, a task that identifies if two texts are written by the same author. The study demonstrates that contrastive learning methods can surpass traditional classification-based approaches, achieving high accuracy on established benchmarks.
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This research explores using contrastive learning for authorship verification, a task that identifies if two texts are written by the same author. The study demonstrates that contrastive learning methods can surpass traditional classification-based approaches, achieving high accuracy on established benchmarks.
Imagine trying to identify if two songs were composed by the same artist just by listening to their unique musical signatures—their choice of instruments, rhythm, and melodic patterns. Contrastive learning works similarly, learning to distinguish between the 'signatures' or stylistic nuances of different authors in text.
How Contrastive Learning Verifies Authorship
- 11. **Embed Text Pairs:** Two text snippets are fed into a neural network (like ModernBERT) to generate numerical representations (embeddings) for each.
- 22. **Measure Similarity:** The system then calculates how similar these two embeddings are. If they are from the same author, their embeddings should be very close; if from different authors, they should be far apart.
- 33. **Optimize Distance:** During training, the model is taught to pull embeddings of same-author texts closer together and push embeddings of different-author texts further apart, using a contrastive loss function.
- 44. **Verify:** For a new pair of texts, if their embeddings are sufficiently close, the system verifies them as being from the same author.
L = Σᵢ_=₁^N (yᵢ · D(xᵢ, xᵢ^+) + (1-yᵢ) · max(0, m - D(xᵢ, xᵢ^-)))
This formula aims to minimize the distance $D$ between positive pairs $(x_i, x_i^+)$ (same author, $y_i=1$) and maximize the distance between negative pairs $(x_i, x_i^-)$ (different authors, $y_i=0$), up to a margin $m$.
In a legal context, authorship verification could be used to determine if two anonymous documents were written by the same individual, providing crucial evidence. For instance, comparing a ransom note to a suspect's known writings.
Contrastive learning's ability to learn robust, discriminative representations of writing styles makes it highly effective for authorship verification, outperforming traditional classification by focusing on similarity and dissimilarity.