This paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT21 shared news translation task. We participate in six directions: English to/from Hausa, Hindi to/from Bengali and Zulu to/from Xhosa. Our submitted systems are unconstrained and focus on multilingual translation odel, backtranslation and forward-translation. We also apply rules and language model to filter monolingual, parallel sentences and synthetic sentences.

This paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT20 shared news translation task. We participate in four directions: English to (Khmer and Pashto) and (Khmer and Pashto) to English. Further, we get the best BLEU scores in the directions of English to Pashto, Pashto to English and Khmer to English (13.1, 23.1 and 25.5 respectively) among all the participants. Our submitted systems are unconstrained and focus on mBART (Multilingual Bidirectional and Auto-Regressive Transformers), back-translation and forward-translation. Also, we apply rules, language model and RoBERTa model to filter monolingual, parallel sentences and synthetic sentences. Besides, we validate the difference of the vocabulary built from monolingual data and parallel data.

This paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT19 shared news translation task. We participate in six directions: English to (Gujarati, Lithuanian and Finnish) and (Gujarati, Lithuanian and Finnish) to English. Further, we get the best BLEU scores in the directions of English to Gujarati and Lithuanian to English (28.2 and 36.3 respectively) among all the participants. The submitted systems mainly focus on back-translation, knowledge distillation and reranking to build a competitive model for this task. Also, we apply language model to filter monolingual data, back-translated data and parallel data. The techniques we apply for data filtering include filtering by rules, language models. Besides, We conduct several experiments to validate different knowledge distillation techniques and right-to-left (R2L) reranking.

This paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT18 shared news translation task. We participated in the English-to-Chinese direction and get the best BLEU (43.8) scores among all the participants. The submitted system focus on data clearing and techniques to build a competitive model for this task. Unlike other participants, the submitted system are mainly relied on the data filtering to obtain the best BLEU score. We do data filtering not only for provided sentences but also for the back translated sentences. The techniques we apply for data filtering include filtering by rules, language models and translation models. We also conduct several experiments to validate the effectiveness of training techniques. According to our experiments, the Annealing Adam optimizing function and ensemble decoding are the most effective techniques for the model training.

In this paper, we describe GTCOM’s neural machine translation(NMT) systems for the International Workshop on Spoken Language Translation(IWSLT) 2017. We participated in the English-to-Chinese and Chinese-to-English tracks in the small data condition of the bilingual task and the zero-shot condition of the multilingual task. Our systems are based on the encoder-decoder architecture with attention mechanism. We build byte pair encoding (BPE) models in parallel data and back-translated monolingual training data provided in the small data condition. Other techniques we explored in our system include two deep architectures, layer nomalization, weight normalization and training models with annealing Adam, etc. The official scores of English-to-Chinese, Chinese-to-English are 28.13 and 21.35 on test set 2016 and 28.30 and 22.16 on test set 2017. The official scores on German-to-Dutch, Dutch-to-German, Italian-to-Romanian and Romanian-to-Italian are 19.59, 17.95, 18.62 and 20.39 respectively.