Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing   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.

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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg