The ARC - NKUA Submission for the E nglish- U krainian General Machine Translation Shared Task at WMT 22 (2022)

Abstract: The ARC-NKUA (“Athena” Research Center - National and Kapodistrian University of Athens) submission to the WMT22 General Machine Translation shared task concerns the unconstrained tracks of the English-Ukrainian and Ukrainian-English translation directions. The two Neural Machine Translation systems are based on Transformer models and our primary submissions were determined through experimentation with (a) ensemble decoding, (b) selected fine-tuning with a subset of the training data, (c) data augmentation with back-translated monolingual data, and (d) post-processing of the translation outputs. Furthermore, we discuss filtering techniques and the acquisition of additional data used for training the systems.

## introduction neural machine translation (nmt) has achieved significant improvements in translation quality in recent years, especially concerning high-resource language pairs. however, there is a lot of room for research on systems with general translation capabilities, underrepresented domains, low-or medium-resource language pairs, as well as multilingual systems. this year, the former news translation shared task widened in scope by introducing new domains, as well as the english-ukrainian language pair among others. we participated in the wmt22 general machine translation shared task for the unconstrained tracks of the en-uk (english to ukrainian) and uk-en (ukrainian to english) translation directions. the two submitted nmt systems are based on the transformer architecture @xcite and our experiments involve various methods and techniques such as data acquisition, filtering and selection, fine-tuning, ensemble decoding, tagged back-translation of english and ukrainian monolingual sentences and post-processing of the translation outputs. this paper is structured in the following way: in section 2, we describe the parallel and monolingual corpora, as well as the acquisition, selection, filtering and pre-processing techniques that were used in our experiments. section 3 outlines the nmt systems architecture, training parameters and the various experiments on top of our baseline systems. in section 4, we report and discuss the experimental results of the two translation directions we participated in, while section 5 concludes and summarizes our work. ## datasets we participated in the unconstrained tracks of this year's general machine translation shared task for the english-ukrainian and ukrainian-english translation directions. we made use of most of the datasets given by the organizers: corpora from opus foot_0 @xcite , paracrawl v9 foot_1 and elrc -eu acts in ukrainian foot_2 from the elrc-share repository. other parallel resources from this repository that were used in our systems include: we also made use of three monolingual datasets given by the organizers: news crawl foot_4 , leipzig corpora foot_5 and legal ukrainian crawling foot_6 from the elrc-share repository. after manually inspecting the other given dataset, ubertext corpus foot_7 , we decided not to use it for backtranslation (see section 3.2), as most punctuation is missing. instead, we make use of monolingual corpora that we acquired (see section 2.1), as well as the ukrainian monolingual corpus of wikimatrix. ## system overview both submitted systems follow the transformer architecture @xcite and were trained using two rtx 2080 ti gpus with the utilization of the fairseq toolkit @xcite . in the subsections that follow, we describe the training process of both nmt systems, as well as the techniques that we experimented with in order to improve translation quality. ## results we perform the evaluation of our systems using the flores101 test set @xcite and the wmt22 general machine translation test set given by the organizers. scores are reported in terms of the detokenized case-sensitive bleu score @xcite and have been computed with the sacrebleu toolkit 20 @xcite . in ## conclusion in this paper, we have presented the arc-nkua submission to the wmt22 general machine translation shared task for the english to ukrainian and ukrainian to english translation directions. the submitted systems follow the transformer architecture and were determined after experimentation with back-translation, selected fine-tuning, and ensemble decoding. we showed that the corrective effect of fine-tuning with a subset of the training set can ultimately increase the translation quality of a system which has exhibited degradation due to having been exposed to a large number of synthetic data, while it also proved useful for systems trained only with parallel data. our systems underperformed in comparison with other submitted systems, according to automatic scores calculated by the organizers foot_12 , although human judgements will be used for official ranking. in the future, we aim at better investigating the effects of acquiring additional parallel and monolingual data, following different filtering, selection and pre-processing strategies, as well as implementing several techniques which have been generally shown to increase translation quality, but hardware and time constraints did not allow us to experiment upon. possible techniques that could be investigated include reranking, larger nmt model architecture, iterative backtranslation, ensembling models trained on different subsets of the training set and exploiting higherresource similar languages.