e T ranslation’s Submissions to the WMT 22 General Machine Translation Task (2022)

Abstract: The paper describes the NMT models for French-German, English-Ukranian and English-Russian, submitted by the eTranslation team to the WMT22 general machine translation shared task. In the WMT news task last year, multilingual systems with deep and complex architectures utilizing immense amount of data and resources were dominant. This year with the task extended to cover less domain specific text we expected even more dominance of such systems. In the hope to produce competitive (constrained) systems despite our limited resources, this time we selected only medium resource language pairs, which are serviced in the European Commission’s eTranslation system. We took the approach of exploring less resource intensive strategies focusing on data selection and filtering to improve the performance of baseline systems. With our submitted systems our approach scored competitively according to the automatic rankings, except for the the English–Russian model where our submission was only a baseline reference model developed as a by-product of the multilingual setup we built focusing primarily on the English-Ukranian language pair.

## introduction the etranslation team is responsible for the development of machine translation systems providing the translation services of the european commission's etranslation project 1 . this is a building block of the connecting europe facility (cef), with the aim of supporting european and national public administrations' information exchange across language barriers in the eu. the project is described in more details in @xcite . during the previous years the team's participation in the wmt shared tasks allowed us to explore state-of-the-art methods to develop high quality machine translation systems. however, due to strict 1 https://language-tools.ec.europa.eu resource constraints, these systems do not normally carry over to production environments and there has been a continuous search for the right balance between the use of resources in production environments and the best performing but more complex architectures. with the news translation shared task extended to being a general mt task the need for more robustness, coverage and consequently more complexity and resources has further increased. we expect a strong competition in these areas, where teams with modest resources might have some inherent disadvantages. therefore, in this year's experiments we did not consider high resource language pairs (specifically @xmath0, our constant submission in previous years) and opted for the medium resource @xmath1. the latter system originated from a multilingual setup including russian data, so we built and submitted a baseline @xmath2. ## data preparation in this section we briefly describe the base data sets, the general selection and filtering methods we applied to prepare these initial data sets used to train the first baseline models. further data selection and augmentation methods to improve the quality of baseline models are described in section 3.1. we only used the provided parallel and monolingual data, so our submissions all fall into the constrained category. ## trainings in all experiments we used marian, as the core tool of our standard nmt framework in the etranslation service. trainings were run as multi-gpu setups on 4 nvidia v100 gpus with 16gb ram, typically for about 30 epochs. in general, except for the first baseline setups, we built only big transformer models, this year even for back-translation, in the hope of getting better quality output for the higher resource consumption. the development scenario was straightforward without much room for experimenting with different parameters or setups due to limited resource availability: for @xmath3, a single set of 4 member ensembles from big transformers, while in @xmath4, a multilingual model at the first stage, fine tuned on the specific languages at the second stage, with 4 (uk) and 3 (ru) models in an ensemble as our submission systems for these two language pairs. the parameter settings did not change from last year's setup: for most of the hyperparameters we used the default settings in the baseline models for the base transformer architecture in marian foot_4 with dynamic batching and tying all embeddings. in @xmath5, trainings were stopped if sentence-wise normalized cross-entropy on the validation set did not improve in 5 consecutive validation steps. the multilingual systems were stopped after about 40 epochs, and then fine tuned for each target direction until they were stopped to meet the submission deadline. in the big transformer setups, we also followed standard settings for marian, i.e. we doubled the filter size and the number of heads, decreased the learning rate from 0.0003 to 0.0002 and halved the update value for -lr-warmup and -lr-decay-inv-sqrt. following common ranges of subword vocabulary sizes, we set a 32k joint sentencepiece vocabulary for all language pairs. sentencepiece models were trained from 10m random segments. ## results we submitted a constrained system for each of the 3 language pairs. we provide comet @xcite (with the default model wmt20-comet-da), chrf @xcite and bleu @xcite evaluation scores for models at important stages in the development, which reflect how the performance of the models changed as we experimented with the various configurations. foot_7 ## conclusion we described the submissions of the etranslation team to the wmt22 general mt shared task on 3 language pairs: french-german, english-ukrainian and english-russian, the last submission being only a baseline setup for reference, built only as a by-product of the @xmath6. we selected medium resource language pairs and tried to focus on data selection, filtering and evaluation with custom test sets to be able to produce strong constrained systems even with limited resources. in our two competitive systems, first automatic results seemed to justify this approach.