DUTNLP Machine Translation System for WMT 21 Triangular Translation Task (2021)

Abstract: This paper describes DUT-NLP Lab’s submission to the WMT-21 triangular machine translation shared task. The participants are not allowed to use other data and the translation direction of this task is Russian-to-Chinese. In this task, we use the Transformer as our baseline model, and integrate several techniques to enhance the performance of the baseline, including data filtering, data selection, fine-tuning, and post-editing. Further, to make use of the English resources, such as Russian/English and Chinese/English parallel data, the relationship triangle is constructed by multilingual neural machine translation systems. As a result, our submission achieves a BLEU score of 21.9 in Russian-to-Chinese.

## introduction the wmt2021 shared task on translating sentences from russian into chinese provides a challenging mixed-genre test for machine translation systems and a triangular relationship for researchers to evaluate new techniques. the task focuses on translation between non-english languages and optimally mixing direct and indirect parallel resources. in this task, the participants must use only the provided parallel training data and use of other data is not allowed. the provided data is shown in pivot: collect parallel russian-to-english and english-to-chinese data (usually larger than direct data), train two translators (russian-to-english + english-to-chinese), and make a cascade translator from russian to chinese. the dutnlp submission to the constrained data track is based on the mainstream architecture transformer @xcite . according to the scale of datasets, this shared task should be considered as the high-resource translation direction. we use the transformer-big setting for better performance, on the contrary, a low-resource translation task often utilizes the transformer-base due to the limited parallel training data. moreover, to enhance the baseline model and investigate the usage of triangular direction data, we utilize two pipelines for combining english related resources: 1) incorporates english-to-chinese translator into direct translation process to normalize the translation of rare words. 2) adopts a multilingual training strategy to make use of english ↔ x parallel resources. besides, some of the provided russian ↔ chinese parallel corpora are crawled from the web, which has many noise issues. we filter the training bilingual corpora with several techniques, including language model and constrained rules. this paper is structured as follows: section 2 describes variants of models we used in the shared task. in section 3, we introduce the system overview using several techniques for model enhancement, including data pre-processing and filtering, triangular translation strategy and finetuning. in section 4, this paper presents experimental settings, main results and analysis. finally, in section 5 we draw a brief conclusion of our work in the wmt2021 triangular translation task. ## conclusion this paper presents the dutnlp translation systems for wmt2021 russian-to-chinese triangular translation tasks. we investigate various neural architectures and data filtering to build strong baseline systems. then the two triangular translation strategies are used to improve the baselines. we also prove that in-domain finetuning is very effective for this translation task. finally, we discuss the results carefully and analyze the influence of different triangular strategies for further improvement. a number of advanced technologies reported in this paper focus on alleviating the issue of triangle translation. as a result, our system outperforms the strong baseline by 1.48 and 1.4 bleu scores on the validation set and test set, respectively. in the future, we will investigate the technologies in the low-resource scenario and continue to improve the performance of this task through post-evaluation submissions.