A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets

Pervaiz Khan, Imran Razzak, Andreas Dengel, Sheraz Ahmed

In: Artificial Neural Networks and Machine Learning - ICANN 2022 - Proceedings. International Conference on Artificial Neural Networks (ICANN-2022) 31st September 6-9 Bristol United Kingdom Lecture Notes in Computer Science (LNCS) 13530 Springer 2022.


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.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence