[1903.07445] An Exploration of State-of-the-art Methods for Offensive Language Detection
In the end, we found that building a convolutional model or using a state-of-art model which we just modified for this task yielded the best results.
Abstract: We provide a comprehensive investigation of different custom and
off-the-shelf architectures as well as different approaches to generating
feature vectors for offensive language detection. We also show that these
approaches work well on small and noisy datasets such as on the Offensive
Language Identification Dataset (OLID), so it should be possible to use them
for other applications.
‹Figure 1: Confusion matrix for the MLP Classifier when using both feature vectors. (Simple Classifiers)›