Better OOV Translation with Bilingual Terminology Mining

Matthias Huck, Viktor Hangya, Alexander Fraser

ACL 2019

Unseen words, also called out-of-vocabulary words (OOVs), are
difficult for machine translation. In neural machine translation,
byte-pair encoding can be used to represent OOVs, but they are still
often incorrectly translated. We improve the translation of OOVs in
NMT using easy-to-obtain monolingual data. We look for OOVs in the
text to be translated and translate them using simple-to-construct
bilingual word embeddings (BWEs). In our MT experiments we take the
5-best candidates, which is motivated by intrinsic mining experiments.
Using all five of the proposed target language words as queries we
mine target-language sentences. We then back-translate, forcing the
back-translation of each of the five proposed target-language
OOV-translation-candidates to be the original source-language OOV. We
show that by using this synthetic data to fine-tune our system the
translation of OOVs can be dramatically improved. In our experiments
we use a system trained on Europarl and mine sentences containing
medical terms from monolingual data.