An AMR Aligner Tuned by Transition-based Parser

EMNLP 2018 Yijia LiuWanxiang CheBo ZhengBing QinTing Liu

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... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Amr Parsing LDC2014T12: Transition-based+improved aligner+ensemble F1 Newswire 0.73 # 2
Amr Parsing LDC2014T12: Transition-based+improved aligner+ensemble F1 Full 0.68 # 2