Multiword Expression aware Neural Machine Translation

Andrea Zaninello, Alexandra Birch


Abstract
Multiword Expressions (MWEs) are a frequently occurring phenomenon found in all natural languages that is of great importance to linguistic theory, natural language processing applications, and machine translation systems. Neural Machine Translation (NMT) architectures do not handle these expressions well and previous studies have rarely addressed MWEs in this framework. In this work, we show that annotation and data augmentation, using external linguistic resources, can improve both translation of MWEs that occur in the source, and the generation of MWEs on the target, and increase performance by up to 5.09 BLEU points on MWE test sets. We also devise a MWE score to specifically assess the quality of MWE translation which agrees with human evaluation. We make available the MWE score implementation – along with MWE-annotated training sets and corpus-based lists of MWEs – for reproduction and extension.
Anthology ID:
2020.lrec-1.471
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3816–3825
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.471
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.471.pdf