@inproceedings{sheth-etal-2021-bootstrapping,
title = "Bootstrapping Multilingual {AMR} with Contextual Word Alignments",
author = "Sheth, Janaki and
Lee, Young-Suk and
Fernandez Astudillo, Ram{\'o}n and
Naseem, Tahira and
Florian, Radu and
Roukos, Salim and
Ward, Todd",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.30",
doi = "10.18653/v1/2021.eacl-main.30",
pages = "394--404",
abstract = "We develop high performance multilingual Abstract Meaning Representation (AMR) systems by projecting English AMR annotations to other languages with weak supervision. We achieve this goal by bootstrapping transformer-based multilingual word embeddings, in particular those from cross-lingual RoBERTa (XLM-R large). We develop a novel technique for foreign-text-to-English AMR alignment, using the contextual word alignment between English and foreign language tokens. This word alignment is weakly supervised and relies on the contextualized XLM-R word embeddings. We achieve a highly competitive performance that surpasses the best published results for German, Italian, Spanish and Chinese.",
}
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<abstract>We develop high performance multilingual Abstract Meaning Representation (AMR) systems by projecting English AMR annotations to other languages with weak supervision. We achieve this goal by bootstrapping transformer-based multilingual word embeddings, in particular those from cross-lingual RoBERTa (XLM-R large). We develop a novel technique for foreign-text-to-English AMR alignment, using the contextual word alignment between English and foreign language tokens. This word alignment is weakly supervised and relies on the contextualized XLM-R word embeddings. We achieve a highly competitive performance that surpasses the best published results for German, Italian, Spanish and Chinese.</abstract>
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%0 Conference Proceedings
%T Bootstrapping Multilingual AMR with Contextual Word Alignments
%A Sheth, Janaki
%A Lee, Young-Suk
%A Fernandez Astudillo, Ramón
%A Naseem, Tahira
%A Florian, Radu
%A Roukos, Salim
%A Ward, Todd
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F sheth-etal-2021-bootstrapping
%X We develop high performance multilingual Abstract Meaning Representation (AMR) systems by projecting English AMR annotations to other languages with weak supervision. We achieve this goal by bootstrapping transformer-based multilingual word embeddings, in particular those from cross-lingual RoBERTa (XLM-R large). We develop a novel technique for foreign-text-to-English AMR alignment, using the contextual word alignment between English and foreign language tokens. This word alignment is weakly supervised and relies on the contextualized XLM-R word embeddings. We achieve a highly competitive performance that surpasses the best published results for German, Italian, Spanish and Chinese.
%R 10.18653/v1/2021.eacl-main.30
%U https://aclanthology.org/2021.eacl-main.30
%U https://doi.org/10.18653/v1/2021.eacl-main.30
%P 394-404
Markdown (Informal)
[Bootstrapping Multilingual AMR with Contextual Word Alignments](https://aclanthology.org/2021.eacl-main.30) (Sheth et al., EACL 2021)
ACL
- Janaki Sheth, Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, and Todd Ward. 2021. Bootstrapping Multilingual AMR with Contextual Word Alignments. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 394–404, Online. Association for Computational Linguistics.