Leveraging Wikipedia article evolution for promotional tone detection
Christine de Kock, Andreas G. Vlachos · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
Detecting biased language is useful for a variety of applications, such as identifying hyperpartisan news sources or flagging onesided rhetoric.In this work we introduce WikiEvolve, a dataset for document-level promotional tone detection in English.Unlike previously proposed datasets, it contains seven versions of the same article from Wikipedia, from different points in its revision history; one with promotional tone, and six without it.We adapt the gradient reversal layer framework to encode two article versions simultaneously, and thus leverage the training signal present in the multiple versions.In our experiments, our proposed adaptation of gradient reversal improves the accuracy of four different architectures on both in-domain and outof-domain evaluation.