Learning Representations for Detecting Abusive Language
This paper discusses the question whether it is possible to learn a generic representation that is useful for detecting various types of abusive language. The approach is inspired by recent advances in transfer learning and word embeddings, and we learn representations from two different datasets containing various degrees of abusive language. We compare the learned representation with two standard approaches; one based on lexica, and one based on data-specific $n$-grams. Our experiments show that learned representations \textit{do} contain useful information that can be used to improve detection performance when training data is limited.
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