NLP with Transformers and CNN

Detecting Evidence of Gender Discrimination in Fijian Court Documents

Chris Sexton and Greg Tozzi

Advances in natural language processing (NLP) and deep learning techniques provide practitioners with an expanded set of options for document classification. This paper leverages recent research in this area, applying convolutional neural networks and BERT variants against a challenging real world dataset to evaluate how well these approaches perform against traditional machine learning approaches. We show that, for these data, state-of-the-art techniques can enjoy real advantages over more traditional techniques, but the effect is smaller than one might expect.

Code available on GitHub

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