Layered Insights: Generalizable Analysis of Human Authorial Style by Leveraging All Transformer Layers

摘要

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 two popular authorship attribution models and three evaluation datasets, 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 a much stronger performance. Our analysis gives further insights into how our model’s different layers get specialized in representing certain linguistic aspects that we believe benefit the model when tested out of the domain.