An AMR Aligner Tuned by Transition-based Parser
Computation and Language
2018-10-09 v1
Abstract
In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. Our aligner is further tuned by our oracle parser via picking the alignment that leads to the highest-scored achievable AMR graph. Experimental results show that our aligner outperforms the rule-based aligner in previous work by achieving higher alignment F1 score and consistently improving two open-sourced AMR parsers. Based on our aligner and transition system, we develop a transition-based AMR parser that parses a sentence into its AMR graph directly. An ensemble of our parsers with only words and POS tags as input leads to 68.4 Smatch F1 score.
Keywords
Cite
@article{arxiv.1810.03541,
title = {An AMR Aligner Tuned by Transition-based Parser},
author = {Yijia Liu and Wanxiang Che and Bo Zheng and Bing Qin and Ting Liu},
journal= {arXiv preprint arXiv:1810.03541},
year = {2018}
}
Comments
EMNLP2018