Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based

AbstractSequence-to-sequence models are widely used to train Abstract Meaning Representation (Banarescu et al.,2013, AMR) parsers. To train such models, AMR graphs have to be linearized into a one-line text format. While Penman encoding is widely used for this purpose, we argue that it has limitations: 1) for deep graphs, some closely related nodes are located far apart in the linearized text 2) Penman’s tree-based encoding necessitates inverse roles to handle node re-entrancy, doubling the number of relation types to predict. To address these issues, we propose a triple-based linearization method and compare its efficiency by training an AMR parser with both approaches. Although triple is well suited to represent a graph, our results show that it does not yet improve performance on deeper or longer graphs. It suggests room for improvement in its design to better compete with Penman’s concise representation and explicit encoding of a nested graph structure.