AMR Parsing As Sequence-to-graph Transduction
Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin van Durme . Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019 – 114 citations
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).