Cross-Lingual Abstract Meaning Representation Parsing

Marco Damonte, Shay B. Cohen


Abstract
Abstract Meaning Representation (AMR) research has mostly focused on English. We show that it is possible to use AMR annotations for English as a semantic representation for sentences written in other languages. We exploit an AMR parser for English and parallel corpora to learn AMR parsers for Italian, Spanish, German and Chinese. Qualitative analysis show that the new parsers overcome structural differences between the languages. We further propose a method to evaluate the parsers that does not require gold standard data in the target languages. This method highly correlates with the gold standard evaluation, obtaining a Pearson correlation coefficient of 0.95.
Anthology ID:
N18-1104
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1146–1155
Language:
URL:
https://aclanthology.org/N18-1104
DOI:
10.18653/v1/N18-1104
Bibkey:
Cite (ACL):
Marco Damonte and Shay B. Cohen. 2018. Cross-Lingual Abstract Meaning Representation Parsing. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1146–1155, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Cross-Lingual Abstract Meaning Representation Parsing (Damonte & Cohen, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1104.pdf
Video:
 http://vimeo.com/282336638
Code
 mdtux89/amr-eager-multilingual