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.