论文标题
一种培训各种语言模型的新型方法，以提及推文的分类
A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets
论文作者
论文摘要
健康提到的分类涉及给定文本中的疾病单词中的疾病检测。但是，疾病词的非健康和比喻使用为任务增加了挑战。最近，充当正规化手段的对抗性培训在许多NLP任务中都广受欢迎。在本文中，我们提出了一种新颖的方法来培训语言模型以提及涉及对抗训练的推文的分类。我们通过使用高斯噪声在各个级别的推文示例中添加扰动来生成对抗示例。此外，我们利用对比度损失作为额外的目标函数。我们在PHM2017数据集扩展版本上评估了建议的方法。结果表明，我们提出的方法在基线方法上显着提高了分类器的性能。此外，我们的分析表明，在较早的层上添加噪声会提高模型的性能，而在中间层增加噪声会恶化模型的性能。最后，向最终层添加噪声比中间层噪声添加更好。
Health mention classification deals with the disease detection in a given text containing disease words. However, non-health and figurative use of disease words adds challenges to the task. Recently, adversarial training acting as a means of regularization has gained popularity in many NLP tasks. In this paper, we propose a novel approach to train language models for health mention classification of tweets that involves adversarial training. We generate adversarial examples by adding perturbation to the representations of transformer models for tweet examples at various levels using Gaussian noise. Further, we employ contrastive loss as an additional objective function. We evaluate the proposed method on the PHM2017 dataset extended version. Results show that our proposed approach improves the performance of classifier significantly over the baseline methods. Moreover, our analysis shows that adding noise at earlier layers improves models' performance whereas adding noise at intermediate layers deteriorates models' performance. Finally, adding noise towards the final layers performs better than the middle layers noise addition.
