Better OOV Translation with Bilingual Terminology Mining
Matthias Huck, Viktor Hangya, Alexander D. Fraser · 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 finetune 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.