Kakao Enterprise’s WMT 21 Machine Translation Using Terminologies Task Submission (2021)

Abstract: This paper describes Kakao Enterprise’s submission to the WMT21 shared Machine Translation using Terminologies task. We integrate terminology constraints by pre-training with target lemma annotations and fine-tuning with exact target annotations utilizing the given terminology dataset. This approach yields a model that achieves outstanding results in terms of both translation quality and term consistency, ranking first based on COMET in the En→Fr language direction. Furthermore, we explore various methods such as back-translation, explicitly training terminologies as additional parallel data, and in-domain data selection.

## introduction we participate in the wmt21 machine translation using terminologies task in four language directions, @xmath0, @xmath1, @xmath2. ## results ## conclusion we participate in four language directions for the shared task wmt21 machine translation terminologies. to this end, we explore various techniques, including back-translation, explicitly training with term pairs along with other parallel data, and in-domain data selection to improve translation performance in the covid-19 domain. in particular, for @xmath3, we find that tla outperforms the baseline in terms of exact match accuracy by leveraging terminology constraints. however, all other metric scores (bleu, 1-term) plummeted, implying that the overall translation quality was compromised. we recover this performance loss by introducing a new techniquefine-tuning with eta, and achieve significant improvements in both general translation quality and terminology consistency. we leave it to future work to validate our approach in other languages and reveal the factors behind the benefits of eta finetuning precisely, hopefully, to discover a more suitable design to impose terminology constraints.