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

2021-06-08 17:52:48
Sarah Uhrig, Yoalli Rezepka Garcia, Juri Opitz, Anette Frank
arXiv_AI
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2106.04565

PDF

https://arxiv.org/pdf/2106.04565.pdf

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