PROMT Systems for WMT 21 Terminology Translation Task (2021)

Abstract: This paper describes the PROMT submissions for the WMT21 Terminology Translation Task. We participate in two directions: English to French and English to Russian. Our final submissions are MarianNMT-based neural systems. We present two technologies for terminology translation: a modification of the Dinu et al. (2019) soft-constrained approach and our own approach called PROMT Smart Neural Dictionary (SmartND). We achieve good results in both directions.

## introduction the currently state-of-the-art approach of neural machine translation (nmt) does not inherently allow for explicit control over the system's output. that is why terminology translation has always been a problem for nmt systems. there are several approaches to solving this problem. one common paradigm is constrained decoding @xcite @xcite , where the terminology matches are presented as hard constraints that the beam search must satisfy. constrained decoding has its disadvantages: it is computationally expensive and can deteriorate the translation quality @xcite . another common approach is the one introduced by @xcite : the terminological constraints are provided as input to the nmt as additional annotations inline with the source sentence. these can be considered 'soft' constraints, as there is no guarantee that the nmt system will indeed produce an output containing them. in this paper we describe two approaches to terminology translation. first, we propose a modification of the @xcite approach. second, we introduce our own technology promt smart neural dictionary (smartnd) aimed at handling terminology translation. the paper is organized as follows: in section 2 we describe the systems built for the task and the data we used. in section 3 we describe our technologies for terminology translation. in section 4 we present and discuss the results. we conclude the paper with discussion for possible future work in section 5. ## systems overview we submitted two single baseline transformerbased @xcite systems trained with the mariannmt @xcite toolkit: english-russian and english-french. we use all parallel data allowed by the organizers. the final systems have the same architecture: we use a shared vocabulary of sizes 16k and 32k for the english-french and english-russian systems respectively. we use the opennmt toolkit @xcite version of byte pair encoding (bpe) @xcite for subword segmentation. we use the devsets provided by the organizers as our development sets. we build intermediate models to obtain backtranslations @xcite for our final systems. we use iterative back-translation for the english-russian system. the intermediate models are trained using sentencepiece @xcite for subword segmentation as we noticed that sentencepiece-based models are more robust in low and middle-resource ## terminology translation in this section we describe our two approaches to terminology handling in detail. ## results and discussion in this section we present the results on the dev and test sets both in terms of automatic and human evaluation and discuss advantages and drawbacks of our approaches. the results of automatic evaluation on the dev sets according to the tool @xcite provided by the organizers are presented in ## conclusions and future work in this paper we presented our submissions for the wmt21 shared terminology translation task. we show good results in both directions we participate (english-french and english-russian). we are planning to make more thorough analysis of the results of our work on both the dev and test sets. we are also planning to try the lemmatized approach as described in @xcite .