# Separating Style from Substance: Enhancing Cross-Genre Authorship Attribution through Data Selection and Presentation

arXiv: https://arxiv.org/abs/2408.05192 · HTML: https://mlsanity.com/paper/2408.05192/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [On the State of the Art in Authorship Attribution and Authorship Verification](https://mlsanity.com/paper/2209.06869/) (2022)
- [Siamese Networks for Large-Scale Author Identification](https://mlsanity.com/paper/1912.10616/) (2019)

## Built on by (most cited first, 7 of 7)

- [Leveraging Multilingual Training for Authorship Representation: Enhancing Generalization across Languages and Domains](https://mlsanity.com/paper/2509.16531/) (2025)
- [Cross-Genre Authorship Attribution via LLM-Based Retrieve-and-Rerank](https://mlsanity.com/paper/2510.16819/) (2025)
- [A Stylometric Application of Large Language Models](https://mlsanity.com/paper/2510.21958/) (2025)
- [A Comparative Study of LLM Prompting and Fine-Tuning for Cross-genre Authorship Attribution on Chinese Lyrics](https://mlsanity.com/paper/2511.21930/) (2025)
- [Interpreting Style Representations via Style-Eliciting Prompts](https://mlsanity.com/paper/2606.05716/) (2026)
- [Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion](https://mlsanity.com/paper/2606.10099/) (2026)
- [STEB: Style Text Embedding Benchmark](https://mlsanity.com/paper/2606.31741/) (2026)

## Abstract (arXiv)

The task of deciding whether two documents are written by the same author is challenging for both machines and humans. This task is even more challenging when the two documents are written about different topics (e.g. baseball vs. politics) or in different genres (e.g. a blog post vs. an academic article). For machines, the problem is complicated by the relative lack of real-world training examples that cross the topic boundary and the vanishing scarcity of cross-genre data. We propose targeted methods for training data selection and a novel learning curriculum that are designed to discourage a model's reliance on topic information for authorship attribution and correspondingly force it to incorporate information more robustly indicative of style no matter the topic. These refinements yield a 62.7% relative improvement in average cross-genre authorship attribution, as well as 16.6% in the per-genre condition.