A Two Stage Approach for AMR Parsing Using the Concept Inference Order

Roxana Pop, Florin Cristian Macicasan, Camelia Lemnaru

Abstract: In this paper we introduce a simple, pipeline graph-based approach for AMR parsing. Transition-based parsers have been researched intensively for the task of AMR parsing since the introduction of AMR and its associated sembank in 2013, but more recently there's been a shift towards graph based approaches, which seem to do better on the task. However, these graph-based approaches often employ large models, trained end-to-end. We intend to see the limitations of a small, pipeline architecture. We use an attention-based sequence-to-sequence model for predicting an ordered list of nodes, then predict the heads for each node in a subsequent step.