AMR Parsing as Sequence-to-Graph Transduction
Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme · 2019
We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction.Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data.Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3% F1 on LDC2017T10) and AMR 1.0 (70.2% F1 on LDC2014T12).