论文标题
Semeval-2020任务的问题Conconquero 12：变压器和基于软标签的方法
problemConquero at SemEval-2020 Task 12: Transformer and Soft label-based approaches
论文作者
论文摘要
在本文中，我们介绍了由我们的团队问题Conconquero针对Semeval-2020共享任务提交的各种系统。我们参加了Arenseval-2020的所有三个子任务，我们在评估阶段的最终提交包括基于变压器的方法和基于软标签的方法。为子任务A的每种语言（进攻性推文标识）提交了基于BERT的微调模型。提交了基于罗伯塔的子任务B（犯罪类型的自动分类）的微调模型。我们提交了两个用于子任务C（进攻目标识别）的模型，一种使用软标签，另一种使用基于BERT的微型模型。我们在子任务A中的排名是37分中的希腊语19，土耳其-22中的46个，丹麦26，39个中的丹麦26，阿拉伯语39，53个中的阿拉伯语-39，在85分中的英式20名。我们在子任务中获得了43个中的28个。我们在39个子任务中获得了39个最佳排名。
In this paper, we present various systems submitted by our team problemConquero for SemEval-2020 Shared Task 12 Multilingual Offensive Language Identification in Social Media. We participated in all the three sub-tasks of OffensEval-2020, and our final submissions during the evaluation phase included transformer-based approaches and a soft label-based approach. BERT based fine-tuned models were submitted for each language of sub-task A (offensive tweet identification). RoBERTa based fine-tuned model for sub-task B (automatic categorization of offense types) was submitted. We submitted two models for sub-task C (offense target identification), one using soft labels and the other using BERT based fine-tuned model. Our ranks for sub-task A were Greek-19 out of 37, Turkish-22 out of 46, Danish-26 out of 39, Arabic-39 out of 53, and English-20 out of 85. We achieved a rank of 28 out of 43 for sub-task B. Our best rank for sub-task C was 20 out of 39 using BERT based fine-tuned model.
