PROMT Systems for WMT 22 General Translation Task (2022)

Abstract: The PROMT systems are trained with the MarianNMT toolkit. All systems use the transformer-big configuration. We use BPE for text encoding, the vocabulary sizes vary from 24k to 32k for different language pairs. All systems are unconstrained. We use all data provided by the WMT organizers, all publicly available data and some private data. We participate in four directions: English-Russian, English-German and German-English, Ukrainian-English.

## introduction the wmt shared general translation task is an annual event where different companies and researchers build and test their systems on the test sets provided by the organizers. this year the task has shifted from news to the general domain. we participate in four directions: english to russian, english to german and back, and ukrainian to english. we build the transformer-big models for the first time. we also explore new data filtering techniques, data preparation and model training strategies. the rest of the paper is organized as follows: in section 2 we describe in detail the systems we submitted to the shared task. in section 3 we present and discuss the results. we conclude the paper in section 4 with discussion for possible future work. ## systems overview all of our wmt22 submissions are @xcite transformer-big @xcite systems. we use the opennmt toolkit @xcite version of byte pair encoding (bpe) @xcite for subword segmentation. our bpe models are case-insensitive, we use special tokens in the source and target sides to process case (see @xcite for details). all of the systems are unconstrained, i.e. we use all data provided by the wmt organizers, all publicly available data and some private data crawled from different web-sources. this year we use the dual conditional crossentropy @xcite ) method for data filtering. we extend the method as proposed by the author and build neural language models for both source and target languages. we also augment our training data with two types of synthetic data: 1) back-translations @xcite and 2) synthetic data with placeholders as described in @xcite . the back-translations are obtained using the previous versions of our nmt models which are baseline transformers trained with less data (and without some up-to-date data like the news 2021 corpora from statmt.org). we also tag all our synthetic data with special tokens at the beginning of the source sentences as described in @xcite . all models are trained with guided alignment which is used at translation time to handle named entities and document formatting. we obtain promt systems for wmt22 shared general translation task alexander molchanov, vladislav kovalenko & natalia makhamalkina promt llc 17e uralskaya str. building 3, 199155, st. petersburg, russia first.last@promt.ru the details regarding different directions can be found in the next section. ## results and discussion the results are presented in as we can see, we clearly outperform our baselines (i.e. previous versions of the models). the gains we observe, however, are not that large. we notice that our submitted models have some problems with translation of colloquial content compared to the previous versions. this can be explained by our data preparation scheme. as we have already mentioned above, we want our models to translate formal text better and thus 'sacrifice' colloquial data. the examples of such degradations are presented in two other examples illustrate bad choice of meaning for specific words from the fine-tuned translation model ('screwed' is translated literally as if the kid was attached to something with a screwdriver; 'кредит' is a word from the financial domain which is inappropriate in this context). we should also note that the gain from the transformer-big configuration for the ukrainian-english model is not that large according to the automatic scores and our human evaluation. we think this is because the synthetic translations obtained from the english-russian data with the russian-ukrainian model are ultimately not of perfect quality. ## conclusions and future work in this paper we presented our submissions for the wmt22 shared general translation task. we show good results in all directions we participate. we clearly outperform our baselines in all directions. a detailed analysis of the translations shows us that we lose quality in translation of colloquial speech. we plan to carefully select colloquial data of very high quality and use it for the general-domain language models for dual cross-entropy data selection. we also plan to train a transformer-big russian-ukrainian model and rebuild the synthetic translations for the ukrainian-english model in the future.