Autor:	Zhang, Sheng, Ma, Xutai, Duh, Kevin, Van Durme, Benjamin
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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). Comment: Accepted at ACL 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1905.08704 Zobrazit plný text záznamu View this record from Arxiv