KDELAB at S em E val-2020 Task 12: A System for Estimating Aggression of Tweets Using Two Layers of BERT Features (2020)

Abstract: In recent years, with the development of social network services and video distribution services, there has been a sharp increase in offensive posts. In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval- 2020 Task 12. Our system precise classification by using features extracted from two different layers of a pre-trained model, the BERT-large, and ensemble them.

## introduction the number of offensive posts has been increasing rapidly due to the wide spread of social networking services such as facebook, twitter, and youtube, where obvious and/or latent offensive texts are accompanied. it is necessary to filter the offensive postings because they can offend users and reduce their satisfaction with the service. manual filtering is expensive, laborious, and time-consuming. thus, the demand for automatic filtering has been increasing year by year. marcos et al. presented semeval2020 task12 @xcite , which includes three sub-tasks. sub-task a is an estimate of the aggressiveness of a tweet, sub-taskb is an estimate of whether a tweet is attacked, and sub-task c is an estimate of the target of a tweet's attack. ## related work a number of studies have been conducted to estimate the aggressiveness of documents. one study classifies facebook and twitter posts into three classes: explicitly offensive, covertly offensive, and nonoffensive @xcite , and another identifies hate speech. at kaggle, a competition was held to classify offensive comments into six classes: toxic, more toxic, obscene, threatening, insulting, and personal attack. in task 6 of semeval2019 @xcite , a competition was held for twitter posts using a dataset with hierarchical labeling focusing on the target of the attack. ## proposed method in the following section, we describe our proposed model for estimating the aggressiveness and the targets of tweets.