Team Rouges at SemEval-2020 Task 12: Cross-lingual Inductive Transfer to Detect Offensive Language

Tanvi Dadu,Kartikey Pant

doi:10.18653/v1/2020.semeval-1.290

Abstract

With the growing use of social media and its availability, many instances of the use of offensive language have been observed across multiple languages and domains. This phenomenon has given rise to the growing need to detect the offensive language used in social media cross-lingually. In OffensEval 2020, the organizers have released the multilingual Offensive Language Identification Dataset (mOLID), which contains tweets in five different languages, to detect offensive language. In this work, we introduce a cross-lingual inductive approach to identify the offensive language in tweets using the contextual word embedding XLM-RoBERTa (XLM-R). We show that our model performs competitively on all five languages, obtaining the fourth position in the English task with an F1-score of 0.919 and eighth position in the Turkish task with an F1-score of 0.781. Further experimentation proves that our model works competitively in a zero-shot learning environment, and is extensible to other languages.

Highlights

The prevalence of social media has made public commentary a critical aspect in shaping public opinion
This paper addresses the challenge put forward in the Multilingual Offensive Language Identification in Social Media shared task-organized at SemEval 2020 (Zampieri et al, 2020)
We provide a comprehensive statistical analysis of the multilingual Offensive Language Identification Dataset

Summary

Introduction

The prevalence of social media has made public commentary a critical aspect in shaping public opinion. Freedom of speech is often advocated, offensive language in social media is unacceptable. Social media platforms and online communities are laden with offensive comments. This phenomenon results in the need for computationally identifying offense, aggression, and hate-speech in user-generated content in multiple languages. This paper addresses the challenge put forward in the Multilingual Offensive Language Identification in Social Media shared task-organized at SemEval 2020 (Zampieri et al, 2020). The theme of the problem is to identify the offensive language in tweets in Arabic, Danish, English, Greek, and Turkish. This shared task is further divided into three sub-tasks. The first task consists of the identification of offensive tweets in a multilingual setting, whereas the other two tasks consist of the categorization of offensive tweets and identification of targets in English

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