# Author Identification using Multi-headed Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1506.04891 · HTML: https://mlsanity.com/paper/1506.04891/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2015.

## Built on by (most cited first, 13 of 13)

- [On the State of the Art in Authorship Attribution and Authorship Verification](https://mlsanity.com/paper/2209.06869/) (2022)
- [Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification](https://mlsanity.com/paper/2310.08123/) (2023)
- [Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution](https://mlsanity.com/paper/2110.14203/) (2021)
- [Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification](https://mlsanity.com/paper/2403.08462/) (2024)
- [AIDBench: A benchmark for evaluating the authorship identification capability of large language models](https://mlsanity.com/paper/2411.13226/) (2024)
- [Can You Fool AI by Doing a 180? unicodex2013 A Case Study on Authorship Analysis of Texts by Arata Osada](https://mlsanity.com/paper/2207.09085/) (2022)
- [BERT-based Authorship Attribution on the Romanian Dataset called ROST](https://mlsanity.com/paper/2301.12500/) (2023)
- [LG4AV: Combining Language Models and Graph Neural Networks for Author Verification](https://mlsanity.com/paper/2109.01479/) (2021)
- [Authorship identification under domain shift: a survey of stylistic measures and learned author representations](https://mlsanity.com/paper/2310.00436/) (2023)
- [One-shot Style Transfer LLM log-probabilities for Authorship Attribution and Verification](https://mlsanity.com/paper/2510.13302/) (2025)
- [Improving Authorship Verification using Linguistic Divergence](https://mlsanity.com/paper/2103.07052/) (2021)
- [Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation](https://mlsanity.com/paper/2403.11265/) (2024)
- [BN-AuthProf: Benchmarking Machine Learning for Bangla Author Profiling on Social Media Texts](https://mlsanity.com/paper/2412.02058/) (2024)

## Abstract (arXiv)

Recurrent neural networks (RNNs) are very good at modelling the flow of text, but typically need to be trained on a far larger corpus than is available for the PAN 2015 Author Identification task. This paper describes a novel approach where the output layer of a character-level RNN language model is split into several independent predictive sub-models, each representing an author, while the recurrent layer is shared by all. This allows the recurrent layer to model the language as a whole without over-fitting, while the outputs select aspects of the underlying model that reflect their author's style. The method proves competitive, ranking first in two of the four languages.