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

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 2183–2189 Barcelona, Spain (Online), December 12, 2020. 2183 Team Rouges at SemEval-2020 Task 12: Cross-lingual Inductive Transfer to Detect Offensive Language Tanvi Dadu ∗ and Kartikey Pant ∗ Netaji Subhas Institute of Technology, New Delhi International Institute of Information Technology, Hyderabad tanvid.co.16@nsit.net.in kartikey.pant@research.iiit.ac.in Abstract With the growing use of social media and its availability, many instances of the use of offen- sive 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 of- fensive 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 po- sition 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. 1 Introduction The prevalence of social media has made public commentary a critical aspect in shaping public opinion. Although freedom of speech is often advocated, offensive language in social media is unacceptable. Nevertheless, 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. Transfer learning is a methodology to utilize the knowledge acquired from one or more tasks to solve other related tasks. It is ubiquitous in the domain of Natural Language Processing. It can be classified into multiple types, like transductive transfer learning and inductive transfer learning. The former is used when the tasks are the same, but the corresponding domains are different, such as in the case of the cross-lingual learning for similar tasks. On the other hand, the latter is used when the tasks are different, but the domains are similar such as in the case of finetuning of pretrained contextual word embeddings. Cross-lingual inductive learning has been used for many downstream tasks like multi-lingual variants of question answering, text classification, and text generation (Artetxe and Schwenk, 2018; Lample and Conneau, 2019; Conneau et al., 2019). The rationale behind this is that the language with …

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