Towards Precise Lexicon Integration in Neural Machine Translation
Ogün Öz, Maria Sukhareva, Data Analytics Lab, Siemens AG, Nuremberg, Germany · 2021
Terminological consistency is an essential requirement for industrial translation.Highquality, hand-crafted terminologies contain entries in their nominal forms.Integrating such a terminology into machine translation is not a trivial task.The MT system must be able to disambiguate homographs on the source side and choose the correct wordform on the target side.In this work, we propose a simple but effective method for homograph disambiguation and a method of wordform selection by introducing multi-choice lexical constraints.We also propose a metric to measure the terminological consistency of the translation.Our results have a significant improvement over the current SOTA in terms of terminological consistency without any loss of the BLEU score.All the code used in this work will be published as open-source.