论文标题
AIR-JPMC@SMM4H'22：在具有多个基于BERT的模型的推文中对自我报告的亲密伴侣暴力进行分类
AIR-JPMC@SMM4H'22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models
论文作者
论文摘要
本文介绍了我们对SMM4H 2022共享任务的提交，内容涉及在Twitter上（英语）上自我报告的亲密伴侣暴力行为的分类。这项任务的目的是准确确定给定推文的内容是否证明了某人报告自己与亲密伴侣暴力的经历。提交的系统是五个罗伯塔模型组成的集合，每个模型都由其在验证数据集上的各自的F1分数加权。该系统的执行效果比基线要好13％，并且是该共同任务的总体性能最佳系统。
This paper presents our submission for the SMM4H 2022-Shared Task on the classification of self-reported intimate partner violence on Twitter (in English). The goal of this task was to accurately determine if the contents of a given tweet demonstrated someone reporting their own experience with intimate partner violence. The submitted system is an ensemble of five RoBERTa models each weighted by their respective F1-scores on the validation data-set. This system performed 13% better than the baseline and was the best performing system overall for this shared task.
