Fraunhofer SIT@SMM4H’22: Learning to Predict Stances and Premises in Tweets related to COVID-19 Health Orders Using Generative Models

AuthorFrick, Raphael Antonius; Steinebach, Martin
TypeConference Paper
AbstractThis paper describes the system used to predict stances towards health orders and to detect premises in Tweets as part of the Social Media Mining for Health 2022 (SMM4H) shared task. It takes advantage of GPT-2 to generate new labeled data samples which are used together with pre-labeled and unlabeled data to fine-tune an ensemble of GAN-BERT models. First experiments on the validation set yielded good results, although it also revealed that the proposed architecture is more suited for sentiment analysis. The system achieved a score of 0.4258 for the stance and 0.3581 for the premise detection on the test set.
ConferenceWorkshop on Social Media Mining for Health Applications 2022