Translate, then Parse! A Strong Baseline for Cross-Lingual AMR Parsing

Sarah Uhrig, Yoalli Garcia, Juri Opitz, Anette Frank


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 overlooked: 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
Anthology ID:
2021.iwpt-1.6
Volume:
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | IWPT
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
58–64
Language:
URL:
https://aclanthology.org/2021.iwpt-1.6
DOI:
10.18653/v1/2021.iwpt-1.6
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.iwpt-1.6.pdf