ISTIC ’s Triangular Machine Translation System for WMT 2021 (2021)

Abstract: This paper describes the ISTIC’s submission to the Triangular Machine Translation Task of Russian-to-Chinese machine translation for WMT’ 2021. In order to fully utilize the provided corpora and promote the translation performance from Russian to Chinese, the pivot method is used in our system which pipelines the Russian-to-English translator and the English-to-Chinese translator to form a Russian-to-Chinese translator. Our system is based on the Transformer architecture and several effective strategies are adopted to improve the quality of translation, including corpus filtering, data pre-processing, system combination and model ensemble.

## introduction the institute of scientific and technical information of china (istic) participated in the triangular machine translation task of russian-to-chinese in the sixth conference on machine translation 1 @xcite . this paper demonstrates the overall framework of the istic's submission and its technical details. in this evaluation, we adopted the neural machine translation architecture of google transformer @xcite as a part of our system. we use the three parallel corpora released by the evaluation organizer and adopted a two-stage method for data pre-processing. several filtering methods of the corpus are explored to reduce the data noise and improve the data quality. as for model construction, we use the pivot method to get a russian-to-chinese translator by bridging the trained russian-to-english translator and englishto-chinese translator. model averaging @xcite , model ensemble @xcite and reranking @xcite strategies are adopted to generate the final output translation. we removed spaces between words and restored the target language translation results to the prescribed file format in data post-processing. in our experiment, the performance of the system under different settings was compared and further analyzed the experimental results. the structure of this paper is as follows: the second part introduces the technical architecture of our machine translation system; the third part describes the data pre-processing, parameter settings, experimental results, and related analysis; the fourth part gives the conclusion and future work. ## system overview the overall framework of the istic's triangular machine translation system is shown in figure 1 . ## conclusions this paper introduces the main technologies and methods of istic's submission in wmt 2021. to sum up, our model is constructed on the transformer architecture of self-attention mechanism and context-based system combination method. in the aspect of data pre-processing, we explore several corpus filtering methods. in the process of translation output, the strategies of model ensemble and reranking are adopted. experimental results show that these methods can effectively improve the quality of translation. it is worth mentioning that the pivot language translation bridge method