  In cross-lingual Abstract Meaning Representation (AMR) parsing, researchers
develop models that project sentences from various languages onto their AMRs to
capture their essential semantic structures: given a sentence in any language,
we aim to capture its core semantic content through concepts connected by
manifold types of semantic relations. Methods typically leverage large silver
training data to learn a single model that is able to project non-English
sentences to AMRs. However, we find that a simple baseline tends to be
over-looked: translating the sentences to English and projecting their AMR with
a monolingual AMR parser (translate+parse,T+P). In this paper, we revisit this
simple two-step base-line, and enhance it with a strong NMT system and a strong
AMR parser. Our experiments show that T+P outperforms a recent state-of-the-art
system across all tested languages: German, Italian, Spanish and Mandarin with
+14.6, +12.6, +14.3 and +16.0 Smatch points.
