FEW-SHOT LEARNING IN INDONESIAN LANGUAGE DOMAIN TEXT CLASSIFICATION - Mahendra G H, Rayza
Lack of labelled data is a long-standing problem on natural language processing (NLP) field, particularly in low-resource languages, such as Indonesian. Transfer learning via pre-trained transformer-based language model (LM) has been a common approach to address this. The two most popular types of pretrained models are encoder only models like BERT and decoder-only models like GPT. Standard finetuning is the defacto method to use in transfer learning. There is another method however, that can be used in very low amount of data setting, namely the few-shot learning method.