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This document describes our approach to building an Offensive LanguageClassifier. More specifically, the OffensEval 2019 competition required us tobuild three classifiers with slightly different goals:  - Offensive language identification: would classify a tweet as offensive ornot.  - Automatic categorization of offense types: would recognize if the target ofthe offense was an individual or not.  - Offense target identification: would identify the target of the offensebetween an individual, group or other.  In this report, we will discuss the different architectures, algorithms andpre-processing strategies we tried, together with a detailed description of thedesigns of our final classifiers and the reasons we choose them over others.  We evaluated our classifiers on the official test set provided for theOffenseEval 2019 competition, obtaining a macro-averaged F1-score of 0.7189 forTask A, 0.6708 on Task B and 0.5442 on Task C.

Absit invidia verbo: Comparing Deep Learning methods for offensive language