YNU _oxz at S em E val-2020 Task 12: Bidirectional GRU with Capsule for Identifying Multilingual Offensive Language (2020)

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.

## 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 @xcite , malicious provocation @xcite , and offensive language @xcite . 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 @xcite . 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 @xcite , among them, gru is simpler and more efficient than the traditional lstm model @xcite . our model used bidirectional gru (bi-gru) @xcite 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 offensive language identification. section 3 describes the models and data. section 4 presents the experimental results. finally, we summarize in section 5. ## 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 international licence. licence details: http:// creativecommons.org/licenses/by/4.0/ through social media such as twitter, facebook, microblog, etc. @xcite attempted to identify hate speech. on the one hand, users benefit from social media by learning or interacting with other users; on the other hand, they face offensive online content. in light of more recent survey of hate speech and offensive language detection, we recommend @xcite and @xcite . @xcite investigated features widely used for hate speech detection, including simple surface features, word generalization, 88 knowledge-based features, etc. @xcite believed that the field of automatic detection of hate speech and offensive language in text is very important for online social platforms and has unquestionable potential for social impact. @xcite presented the results of hate speech detection using word n-grams and emotional vocabulary, and provided insights into examples of misclassification. in addition to recently published research, a number of related sharing tasks have been organized. among them, gemeval2018 @xcite is about offensive language recognition and aims to promote research on offensive content recognition in german language microblogs. the best team's system is to train three basic classifiers (maximum entropy and two random forest sets) using five disjoint feature sets, and then used the maximum entropy element-level classifier for final classification @xcite . in the semeval-2019 shared tasks hateval @xcite and offenseval @xcite , hateval is a multilingual detection of hate speech against immigrants and women on twitter. fermi team is the best team of hateval. it proposes a svm model with rbf kernel and uses sentence embedding in google general sentence encoder as a function @xcite . offenseval is about the identification and classification of offensive language in social media. the nuli team is the best performing team, they use bert-base without default parameters @xcite . hasoc2019 @xcite is proposed to identify hate speech and offensive content in indo-european languages. its purpose is to develop powerful technologies capable of processing multilingual data and to develop a transfer learning method that can utilize cross-lingual data. the optimal system is a system based on ordered neuron lstm (on-lstm) and attention model and adopts k-folding approach for ensemble @xcite . ## conclusion this year we participated in the english subtask a for multilingual offensive language recognition, which is automatically classifying offensive language in social media. this paper proposed a model for english offensive language recognition. we used the bi-gru model for classification, and used the capsule network to improve the feature extraction capability of the model. although our model overall performance is not the best, preliminary results indicate what we should do next. in future research, we consider the impact of unlabeled datasets on the results, we will consider the introduction of transfer learning, and how to optimize the parameters is also a very important issue. we will also consider offensive language recognition in other languages (greek, arabic, spanish and danish).