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This paper presents our contribution to PolEval 2019 Task 6: Hate speech andbullying detection. We describe three parallel approaches that we followed:fine-tuning a pre-trained ULMFiT model to our classification task, fine-tuninga pre-trained BERT model to our classification task, and using the TPOT libraryto find the optimal pipeline. We present results achieved by these three toolsand review their advantages and disadvantages in terms of user experience. Ourteam placed second in subtask 2 with a shallow model found by TPOT: a~logisticregression classifier with non-trivial feature engineering.

Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish