# BERT-based Authorship Attribution on the Romanian Dataset called ROST

arXiv: https://arxiv.org/abs/2301.12500 · HTML: https://mlsanity.com/paper/2301.12500/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 29 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [The birth of Romanian BERT](https://mlsanity.com/paper/2009.08712/) (2020)
- [On the State of the Art in Authorship Attribution and Authorship Verification](https://mlsanity.com/paper/2209.06869/) (2022)
- [The Importance of Suppressing Domain Style in Authorship Analysis](https://mlsanity.com/paper/2005.14714/) (2020)
- [The Topic Confusion Task: A Novel Scenario for Authorship Attribution](https://mlsanity.com/paper/2104.08530/) (2021)
- [Author Identification using Multi-headed Recurrent Neural Networks](https://mlsanity.com/paper/1506.04891/) (2015)
- [A comparison of several AI techniques for authorship attribution on Romanian texts](https://mlsanity.com/paper/2211.05180/) (2022)

## Built on by (most cited first, 2 of 2)

- [Oldies but Goldies: The Potential of Character N-grams for Romanian Texts](https://mlsanity.com/paper/2506.15650/) (2025)
- [CHiPS: Character Histograms and Positional Signals for Lightweight Authorship Attribution in Romanian Texts](https://mlsanity.com/paper/2607.22884/) (2026)

## Abstract (arXiv)

Being around for decades, the problem of Authorship Attribution is still very much in focus currently. Some of the more recent instruments used are the pre-trained language models, the most prevalent being BERT. Here we used such a model to detect the authorship of texts written in the Romanian language. The dataset used is highly unbalanced, i.e., significant differences in the number of texts per author, the sources from which the texts were collected, the time period in which the authors lived and wrote these texts, the medium intended to be read (i.e., paper or online), and the type of writing (i.e., stories, short stories, fairy tales, novels, literary articles, and sketches). The results are better than expected, sometimes exceeding 87% macro-accuracy.