结合R-drop的文本增强方法用于自报告新冠疫情推文分类
Text Augmentations with R-drop for Classification of Tweets Self Reporting Covid-19
AI总结：
针对社交媒体健康挖掘共享任务的新冠自报告推文分类问题，提出融合多种文本增强与R-drop的分类模型，缓解过拟合并提升效果，测试集F1达0.877，优于任务平均与中位数水平。
摘要：
本文介绍了为2023年社交媒体健康挖掘共享任务构建的模型。我们团队聚焦于第一项任务：对自我报告新冠确诊的推文进行分类。我们的方法采用融合多种文本增强技术的分类模型，并利用R-drop进行数据增强、缓解过拟合，以提升模型效果。我们的最优模型结合了R-drop以及同义词替换、保留词、回译等增强手段，性能优于任务的平均分和中位数分，在测试集上取得了0.877的F1分数。
英文摘要：
This paper presents models created for the Social Media Mining for Health 2023 shared task. Our team addressed the first task, classifying tweets that self-report Covid-19 diagnosis. Our approach involves a classification model that incorporates diverse textual augmentations and utilizes R-drop to augment data and mitigate overfitting, boosting model efficacy. Our leading model, enhanced with R-drop and augmentations like synonym substitution, reserved words, and back translations, outperforms the task mean and median scores. Our system achieves an impressive F1 score of 0.877 on the test set.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
