Semantic Role Labeling

Semantic role labeling (SRL) aims to identify the roles that words play in a sentence, such as agent, patient, or instrument, relative to a predicate (typically a verb). Current research focuses on improving SRL's accuracy and efficiency across multiple languages, often leveraging deep learning models like transformers and exploring techniques like transfer learning and multi-task learning to address data scarcity and improve generalization. Advancements in SRL are crucial for enhancing various natural language processing applications, including machine translation, question answering, and information extraction, by providing a richer, more structured understanding of text.