PROMT Systems for WMT 2020 Shared News Translation Task (2020)

Abstract: This paper describes the PROMT submissions for the WMT 2020 Shared News Translation Task. This year we participated in four language pairs and six directions: English-Russian, Russian-English, English-German, German-English, Polish-English and Czech-English. All our submissions are MarianNMT-based neural systems. We use more data compared to last year and update our back-translations with better models from the previous year. We show competitive results in terms of BLEU in most directions.

## introduction this paper provides an overview of the promt submissions for the wmt 2020 shared news translation task. this year we participate with neural mt systems for the third time. we participate in four language pairs and six directions. we describe our data preparation pipelines, models training setups and present the results on the newstest sets. the paper is organized as follows: section 2 is a brief overview of the submitted systems. section 3 describes the data preparation, preprocessing and statistics in detail. section 4 provides a detailed description of the systems. in section 5 we present and discuss the results. section 6 concludes the paper. ## data we use all data provided by the wmt organizers, private in-house parallel data and other publicly available data, mainly from the opus website @xcite . the human parallel data for the german-english system is exactly the same as for the english-german system, the two systems only have different synthetic back-translated data. this also applies to the english-russian and russian-english systems. we use the tatoeba sets as our validation sets and the newstest2019 is our test set. the reason why we choose the tatoeba corpus for validation is that we aim at building general-domain (and not just news-domain) models. besides, the tatoeba corpus is available for many language pairs beyond the scope of the wmt translation task. we only do fine-tuning for the czech-english system. this will be described in detail in section 3.4 below. ## systems architecture this section describes the trained systems in detail. we train transformer @xcite models for all submitted systems. we use the recipe available at the mariannmt website foot_0 . the system configuration, hyperparameters and training steps follow those in the recipe. we use the transformer-big configuration for the english-russian model. we train single models for all directions. we use the beam of size 6 and the -normalize parameter is set to 0.6. ## results and discussion in this section we present the bleu @xcite scores for our systems on two test sets and the analysis of the results. the scores are presented in we significantly outperform our last year's submissions for the news task. fine-tuning for the czech-english system does not give us significant improvements in terms of bleu. this may be because we didn't perform any data selection this year. we are among the top 10 systems in the @xmath0, however, we are substantially behind the top systems in other directions in terms of bleu. we see two reasons for that. first of all, we pay much more attention to our russian systems, thus, our last year's russian systems had already undergone several iterations of updated backtranslations and retraining and can be considered strong baselines. second, we possess much more private highquality data for the english-russian pair compared to other language pairs. ## conclusions and future work in this paper we have described our submissions for the wmt 2020 shared news translation task. overall we have made six submissions in four language pairs: english-russian, english-german, polish-english and czech-english. we have documented the methodology used to prepare the training data, system training set-ups, the pipeline for handling nes. we show competitive results in most directions. in future we plan to experiment once again with a shared vocabulary for the english-russian models applying transliteration to the source side.