YNU_oxz at SemEval-2020 Task 12: Bidirectional GRU with Capsule for Identifying Multilingual Offensive Language

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 2251–2257 Barcelona, Spain (Online), December 12, 2020. 2251 YNU oxz at SemEval-2020 Task 12: Bidirectional GRU with Capsule for Identifying Multilingual Offensive Language Xiaozhi Ou, Hongling Li∗ School of Information Science and Engineering Yunnan University, Yunnan, P.R. China ∗Corresponding author, honglingli66@126.com Abstract This article describes the system submitted to SemEval-2020 Task 12 OffensEval 2: Multilingual Offensive Language Recognition in Social Media. The task is to classify offensive language in social media. The shared task contains five languages (English, Greek, Arabic, Danish, and Turkish) and three subtasks. We only participated in subtask A of English to identify offensive language. To solve this task, we proposed a system based on a Bidirectional Gated Recurrent Unit (Bi-GRU) with a Capsule model. Finally, we used the K-fold approach for ensemble. Our model achieved a Macro-average F1 score of 0.90969 (ranked 27/85) in subtask A. 1 Introduction Offensive language is ubiquitous in social media, and individuals often uses the anonymity of computer communications for some anti-social network behaviors, including cyberbullying (Xu et al., 2012), mali- cious provocation (Kwok and Wang, 2013), and offensive language (Cheng et al., 2017). The widespread dissemination of offensive content in social media is a cause of concern for governments and many technology companies around the world. One of the most common and effective strategies for solving offensive language problems on the network is to train systems that can recognize such content. SemEval-2020 OffensEval 2 is proposed for multilingual offensive language recognition in social media (Zampieri et al., 2020). The shared task contains three subtasks and five languages (English, Greek, Arabic, Danish, and Turkish), where subtask A is a coarse-grained binary classification, which aims to the identification of offensive language. Participating systems need to divide Tweet into two categories: Offensive (OFF) and Not Offensive (NOT). In this competition, we only participated in subtask A of the English language. We used deep learning to build a bidirectional GRU(Bi-GRU) with Capsule model (Yang et al., 2018), among them, GRU is simpler and more efficient than the traditional LSTM model (Chung et al., 2014). Our model used bidirectional GRU (Bi-GRU) (Bahdanau et al., 2014) to process the sequence from two directions, utilizing both the previous and future context, and capsule is a group of neurons that use vectors to represent parameters, capsule network uses the inner product method to cluster the input features. The rest of this article is organized as follows. Section 2 introduces related work on multilingual offen- sive language identification. Section 3 describes the models and data. Section 4 presents the experimental results. Finally, we summarize in Section 5. 2 Related Work In recent years, offensive language has prevailed on social media, and social media has become the most popular media among users. According to the survey (QEV Analytics and of America, 2009), it was observed that 70% of adolescents used social media sites every day, and users shared their opinions This work is licensed under a Creative Commons Attribution 4.0 …

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