Improving Rare Word Translation With Dictionaries and Attention Masking

Kenneth J Sible, David Chiang


Abstract
In machine translation, rare words continue to be a problem for the dominant encoder-decoder architecture, especially in low-resource and out-of-domain translation settings. Human translators solve this problem with monolingual or bilingual dictionaries. In this paper, we propose appending definitions from a bilingual dictionary to source sentences and using attention masking to link together rare words with their definitions. We find that including definitions for rare words improves performance by up to 1.0 BLEU and 1.6 MacroF1.
Anthology ID:
2024.amta-research.19
Volume:
Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
Month:
September
Year:
2024
Address:
Chicago, USA
Editors:
Rebecca Knowles, Akiko Eriguchi, Shivali Goel
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
225–235
Language:
URL:
https://aclanthology.org/2024.amta-research.19
DOI:
Bibkey:
Cite (ACL):
Kenneth J Sible and David Chiang. 2024. Improving Rare Word Translation With Dictionaries and Attention Masking. In Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 225–235, Chicago, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Improving Rare Word Translation With Dictionaries and Attention Masking (Sible & Chiang, AMTA 2024)
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PDF:
https://aclanthology.org/2024.amta-research.19.pdf