PAN 2020: Style Change Detection Using BERT
Channel: webis
Date: 2020-09-25

Style Change Detection Using BERT—Notebook for PAN at CLEF 2020

Aarish Iyer and Soroush Vosoughi

The Style Change Detection task is very important in the area of authorship profiling, having one of its main applications in plagiarism detection. Specifically, the goal of the task is to detect where (if any) stylistic changes happen in a document which can be used to estimate the number of authors of a given document. In this paper, we present a method for Style Change Detection. We use Google AI’s open source BERT pretrained bidirectional models to tokenize and generate embeddings for the sentences in each document in our dataset and use those to train a random forest classifier. We achieved an F1 score of 0.86 for detecting style changes and an F1 score of 0.64 for detecting multi-author documents on the test set, placing us at the top of the competition for both tasks. The code for this project has been made open source so that it can be used for further research: https://github.com/aarish407/Style-Change-Detection-Using-BERT

Paper: https://pan.webis.de/publications.html#iyer_2020
Workshop: https://pan.webis.de