Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing

• URL: http://arxiv.org/abs/2106.04565v1
• Date: Tue, 8 Jun 2021 17:52:48 GMT
• Title: Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing
• Authors: Sarah Uhrig, Yoalli Rezepka Garcia, Juri Opitz, Anette Frank
• Abstract summary: We develop models that project sentences from various languages onto their AMRs to capture their essential semantic structures. In this paper, we revisit a simple two-step base-line, and enhance it with a strong NMT system and a strong AMR. Our experiments show that T+P outperforms a recent state-of-the-art system across all tested languages.
• Score: 10.495114898741205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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