GTCOM Neural Machine Translation Systems for WMT 22 (2022)

Abstract: GTCOM participates in five directions: English to/from Ukrainian, Ukrainian to/from Czech, English to Chinese and English to Croatian. Our submitted systems are unconstrained and focus on backtranslation, multilingual translation model and finetuning. Multilingual translation model focus on X to one and one to X. We also apply rules and language model to filter monolingual, parallel sentences and synthetic sentences.

## introduction we applied fairseq @xcite as our develop tool and use transformer @xcite as the main architecture. the primary ranking index for submitted systems is bleu @xcite , therefore we apply bleu as the evaluation matrix for our translation system by using sacre-bleu 1 . for data preprocessing, punctuation normalization, tokenization and bpe(byte pair encoding) are applied for all languages. further, we apply truecase model for english, ukrainian, czech and croatian according to the character of each language. regarding to the tokenization, we use polyglot as the tokenizer for ukrainian and croatian, and mosese tokenizer.perl for english and czech. besides, knowledge based rules and language model are also involved to clean parallel data, monolingual data and synthetic data. this paper is arranged as follows. we firstly describe the task and show the data information, then introduce our baseline and multilingual translation model. after that, we describe the conducted experiments in detail in all directions, including data preprocessing, model architecture, back-translation and multilingual translation model. at last, we ## task description the task focuses on bilingual text translation and the provided data is shown in usually, the news translation task will take the human evaluation result as the final ranking index. and this requires each participated team contribute 8 hours of human evaluation for each participating translation direction. for some low resource language directions, it is not very easy for the organizer to employ human translators from the participating team or translation agency. besides, due to the number of sentences in the test set and the quantity of participating teams, it is not possible to employ human evaluation for all the test sets. besides, with recent improvements of mt quality, the organizer decided to move away from testing only in the news domain and we are shifting the wmt focus on testing the general capabilities of mt systems. ## billingual baseline model and multilingual translation model to set a strong baseline for our multilingual model as a comparison. our billingual base-language number of sentences en-hr parallel data 318m en-uk parallel data 13m uk-cs parallel data 4m en monolingual data 40m uk monolingual data 15m cs monolingual data 40m hr monolingual data 13m en-uk development set 997 en-hr development set 997 uk-cs development set 997 the multilingual translation model is almost the same as gtcom2021 @xcite , but focuses on one to x and x to one this year. to obtain a better translation quality, we include russian as the main auxiliary language since russian and ukrainian are very similar. we train four multilingual models: 1. ru-en, uk-en and hr-en to translate uk-en; 2. en-ru, en-uk and en-hr to translate en-uk and en-hr; 3. cs-uk, en-uk and ru-uk to translate cs-uk 4. en-uk; uk-cs and en-cs to translate uk-cs and en-cs. we use joint bpe for all languages in the multilingual model separately. for english to chinese direction, we just test our online system as a comparison with other participating systems. therefore we did not conduct data augmentation, finetuning, or any other adaption experiments. ## result and analysis we notice that when adding russian (a very similar language to ukrainian) into the multilingual corpus, we did not obtain as much improvement as we expect. this is probably because the original english to ukrainian data is rich enough and decreased the positive impact of adding russian data into the multilingual model. ## conclusion this paper describes gtcom's neural machine translation systems for the wmt22 shared general mt task. we applied 3 major techniques to improve the translation quality: back-translation, joint training, and deep multilingual translation model. with these 3 techniques, the final automatic evaluation matrix is shown in