Author Identification using Multi-headed Recurrent Neural Networks

Code

• github.com/pan-webis-de/caravel
  OfficialIn papernone★ 0
• github.com/ShogoAkiyama54/author-identification
  none★ 0
• github.com/shogo54/author-identification
  none★ 0
• github.com/ShogoAkiyama54/author_identification
  none★ 0

Abstract

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.

Tasks

Reproductions