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Bootstrapping Multilingual AMR with Contextual Word Alignments

2021-02-03 18:35:55
Janaki Sheth, Young-Suk Lee, Ramon Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward
arXiv_AI
         
Abstract

We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision. Weachieve this goal by bootstrapping transformer-based multilingual word embeddings, in partic-ular those from cross-lingual RoBERTa (XLM-R large). We develop a novel technique forforeign-text-to-English AMR alignment, usingthe contextual word alignment between En-glish and foreign language tokens. This wordalignment is weakly supervised and relies onthe contextualized XLM-R word embeddings.We achieve a highly competitive performancethat surpasses the best published results forGerman, Italian, Spanish and Chinese.

Abstract (translated)

URL

https://arxiv.org/abs/2102.02189

PDF

https://arxiv.org/pdf/2102.02189.pdf

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