AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERT

Keyword extraction has been an important topic for modern natural language processing. With its applications ranging from ontology generation, fact verification in summarized text, and recommendation systems. While it has had significant data-intensive applications, it is often hampered when the data set is small. Downstream training for keyword extractors is a lengthy process and requires a significant amount of data. Recently, Few-shot Learning (FSL) and Zero-Shot Learning (ZSL) have been proposed to tackle this problem. Therefore, we propose AdaptKeyBERT, a pipeline for training keyword extractors with LLM bases by incorporating the concept of regularized attention into a pre-training phase for downstream domain adaptation.