Embeddia at SemEval-2019 task 6: Detecting hate with neural network and transfer learning approaches

Abstract

SemEval-2019 Task 6 was OffensEval: Identifying and Categorizing Offensive Language in Social Media. The task was further divided into three sub-tasks: offensive language identification, automatic categorization of offense types, and offense target identification. In this paper, we present the approaches used by the Embeddia team, who qualified as fourth, eighteenth and fifth on the three sub-tasks. A different model was trained for each sub-task. For the first sub-task, we used a BERT model fine-tuned on the provided dataset, while for the second and third tasks we developed a custom neural network architecture which combines bag-of-words features and automatically generated sequence-based features. Our results show that combining automatically and manually crafted features fed into a neural architecture outperform transfer learning approach on more unbalanced datasets.