The ARC-NKUA Submission for the English-Ukrainian General Machine Translation Shared Task at WMT22

Proceedings of the Seventh Conference on Machine Translation (WMT), pages 358 - 365 December 7-8, 2022 ©2022 Association for Computational Linguistics Abstract In what follows, we provide an overview of the ARC-NKUA (“Athena” Research Center - National and Kapodistrian University of Athens) submission to the WMT22 General Machine Translation shared task for the EN-UK (English to Ukrainian) and UK-EN (Ukrainian to English) translation directions. We describe how we constructed two Neural Machine Translation systems by training Transformer models (Vaswani et al., 2017), as well as our experiments involving: (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. 1 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 1https://opus.nlpl.eu/ 2https://paracrawl.eu/news/item/17- english-ukrainian-bonus-parallel-corpus Ukrainian) and UK-EN (Ukrainian to English) translation directions. The two submitted NMT systems are based on the Transformer architecture (Vaswani et al., 2017) 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. 2 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 1 (Tiedemann, 2012), ParaCrawl v9 2 and ELRC - EU acts in Ukrainian3 from the ELRC- SHARE repository. Other parallel resources from this repository that were used in our systems include: 3https://elrc-share.eu/repository/eu- acts-in-ukrainian/ The ARC-NKUA submission for the English-Ukrainian General Machine Translation Shared Task at WMT22 Dimitrios Roussis1,2 and Vassilis Papavassiliou1 1Athena Research Center, Athens, Greece 2National and Kapodistrian University of Athens, Greece {dimitris.roussis, vpapa}@athenarc.gr 358 • Multilingual English, French, Polish to Ukrainian Parallel Corpus (processed)4 • Official web-portal of the Parliament of Ukraine, primary legislation5 • Official web-portal of the Parliament of Ukraine, Ukrainian laws in EN6 • Official web-portal …

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