UR_NLP @ HaSpeeDe 2 at EVALITA 2020: Towards Robust Hate Speech Detection with Contextual Embeddings
Julia Hoffmann, Udo Kruschwitz · Accademia University Press eBooks · 2020
We describe our approach to address Task A of the EVALITA 2020 Hate Speech Detection (HaSpeeDe2) challenge. We submitted two runs that are both based on contextual embeddings – which we had chosen due to their effectiveness in solving a wide range of NLP problems. For our baseline run we use stacked embeddings that serve as features in a linear SVM. Our second run is a simple ensemble approach of three SVMs with majority voting. Both approaches outperform the official baselines by a large margin, and the ensemble classifier in particular demonstrates robust performance on different types of test data coming 6th (out of 27 runs) for news headlines and 10th (out of 27) for Twitter feeds.