Impact of Task Adapting on Transformer Models for Targeted Sentiment Analysis in Croatian Headlines

Sofia Lee, Jelke Bloem

Abstract: Transformer models, such as BERT, are often taken off-the-shelf and then fine-tuned on a downstream task. Although this is sufficient for many tasks, low-resource settings require special attention. We demonstrate an approach of performing an extra stage of self-supervised task-adaptive pre-training to a number of Croatian-supporting Transformer models. In particular, we focus on approaches to language, domain, and task adaptation. The task in question is targeted sentiment analysis for Croatian news headlines. We produce new state-of-the-art results (F1 = 0.781), but the highest performing model still struggles with irony and implicature. Overall, we find that task-adaptive pre-training benefits massively multilingual models but not Croatian-dominant models.