Improving Authorship Verification using Linguistic Divergence
arXiv:2103.07052
Abstract
We propose an unsupervised solution to the Authorship Verification task that utilizes pre-trained deep language models to compute a new metric called DV-Distance. The proposed metric is a measure of the difference between the two authors comparing against pre-trained language models. Our design addresses the problem of non-comparability in authorship verification, frequently encountered in small or cross-domain corpora. To the best of our knowledge, this paper is the first one to introduce a method designed with non-comparability in mind from the ground up, rather than indirectly. It is also one of the first to use Deep Language Models in this setting. The approach is intuitive, and it is easy to understand and interpret through visualization. Experiments on four datasets show our methods matching or surpassing current state-of-the-art and strong baselines in most tasks.
arXiv commentPublished in ROMCIR 2021. Workshop held as part of ECIR 2021. March 28 - April 1, 2021
References in corpus (6)
- Efficient Estimation of Word Representations in Vector Space
- Distributed Representations of Words and Phrases and their Compositionality
- An Army of Me: Sockpuppets in Online Discussion Communities
- Author Identification using Multi-headed Recurrent Neural Networks
- Experiments with Neural Networks for Small and Large Scale Authorship Verification
- Detecting Sockpuppets in Deceptive Opinion Spam