  Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph
from textual input. Recently, there has been notable growth in AMR parsing
performance. However, most existing work focuses on improving the performance
in the specific domain, ignoring the potential domain dependence of AMR parsing
systems. To address this, we extensively evaluate five representative AMR
parsers on five domains and analyze challenges to cross-domain AMR parsing. We
observe that challenges to cross-domain AMR parsing mainly arise from the
distribution shift of words and AMR concepts. Based on our observation, we
investigate two approaches to reduce the domain distribution divergence of text
and AMR features, respectively. Experimental results on two out-of-domain test
sets show the superiority of our method.
