AMR Parsing

AMR parsing aims to automatically convert sentences into Abstract Meaning Representations (AMRs), graph-based semantic structures. Current research focuses on improving accuracy, particularly addressing limitations of existing evaluation metrics like Smatch, which may not fully capture semantic meaning. This involves exploring novel model architectures, such as those incorporating hierarchical attention and pointer mechanisms, and refining ensemble methods to enhance robustness and efficiency. Advances in AMR parsing have significant implications for various natural language processing applications, including clinical text analysis and cross-lingual understanding.