论文标题
Semeval-2020任务的Kuisail 12：Bert-CNN，用于社交媒体中的进攻性语音识别
KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media
论文作者
论文摘要
In this paper, we describe our approach to utilize pre-trained BERT models with Convolutional Neural Networks for sub-task A of the Multilingual Offensive Language Identification shared task (OffensEval 2020), which is a part of the SemEval 2020. We show that combining CNN with BERT is better than using BERT on its own, and we emphasize the importance of utilizing pre-trained language models for downstream tasks.我们的系统在阿拉伯语中排名第四，宏平均为0.897，希腊语得分为0.843，而土耳其为0.814的得分为0.843。此外，我们提出了Arabicbert，这是我们与社区共享的一系列阿拉伯语的预训练的变压器语言模型。
In this paper, we describe our approach to utilize pre-trained BERT models with Convolutional Neural Networks for sub-task A of the Multilingual Offensive Language Identification shared task (OffensEval 2020), which is a part of the SemEval 2020. We show that combining CNN with BERT is better than using BERT on its own, and we emphasize the importance of utilizing pre-trained language models for downstream tasks. Our system, ranked 4th with macro averaged F1-Score of 0.897 in Arabic, 4th with score of 0.843 in Greek, and 3rd with score of 0.814 in Turkish. Additionally, we present ArabicBERT, a set of pre-trained transformer language models for Arabic that we share with the community.
