Papers › Enolp musk@SMM4H’22 : Leveraging Pre-trained Language Models for Stance And Premise...
Enolp musk@SMM4H’22 : Leveraging Pre-trained Language Models for Stance And Premise Classification
Millon Das, Archit Mangrulkar, Ishan Manchanda, Manav Kapadnis, Sohan Patnaik
This paper covers our approaches for the Social Media Mining for Health (SMM4H) Shared Tasks 2a and 2b. Apart from the baseline architectures, we experiment with Parts of Speech (PoS), dependency parsing, and Tf-Idf features. Additionally, we perform contrastive pretraining on our best models using a supervised contrastive loss function. In both the tasks, we outperformed the mean and median scores and ranked first on the validation set. For stance classification, we achieved an F1-score of 0.636 using the CovidTwitterBERT model, while for premise classification, we achieved an F1-score of 0.664 using BART-base model on test dataset.
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