Layered Insights: Generalizable Analysis of Authorial Style by Leveraging All Transformer Layers
This work addresses authorship attribution for forensic or literary analysis, but it is incremental as it builds on existing transformer methods.
The authors tackled authorship attribution by leveraging linguistic representations from different transformer layers, achieving new state-of-the-art results with improved robustness on out-of-domain data.
We propose a new approach for the authorship attribution task that leverages the various linguistic representations learned at different layers of pre-trained transformer-based models. We evaluate our approach on three datasets, comparing it to a state-of-the-art baseline in in-domain and out-of-domain scenarios. We found that utilizing various transformer layers improves the robustness of authorship attribution models when tested on out-of-domain data, resulting in new state-of-the-art results. Our analysis gives further insights into how our model's different layers get specialized in representing certain stylistic features that benefit the model when tested out of the domain.