U niversity of T sukuba’s Machine Translation System for IWSLT 20 Open Domain Translation Task (2020)

Abstract: In this paper, we introduce University of Tsukuba’s submission to the IWSLT20 Open Domain Translation Task. We participate in both Chinese→Japanese and Japanese→Chinese directions. For both directions, our machine translation systems are based on the Transformer architecture. Several techniques are integrated in order to boost the performance of our models: data filtering, large-scale noised training, model ensemble, reranking and postprocessing. Consequently, our efforts achieve 33.0 BLEU scores for Chinese→Japanese translation and 32.3 BLEU scores for Japanese→Chinese translation.

## introduction in this paper, we introduce university of tsukuba's submission to the iwslt20 open domain translation task. the goal of this shared task is to promote: the research on translation between asian languages, exploitation of noisy web corpora for machine translation, and smart processing of data and provenance. to have an overview look of the iwslt20 open domain translation task, readers may refer to @xcite for further details. we participated in both @xmath0. it is widely acknowledged that a neural machine translation (nmt) system requires a large amount of training data. meanwhile, the training process of nmt models may consume a long period of time and lots of computing resources. considering the limitation of our computing power, our goal is to boost the performance of nmt systems with fewer and smaller components that require less time and computing resources. our ## data for all of our submissions, we only use datasets provided by the organizers. for training parallel data, we use the concatenation of web crawled parallel filtered and existing parallel. for data augmentation, since the unaligned data are extremely huge, we choose to use the separate side of the pre-filtered sentences (web crawled parallel unfiltered) as monolingual data. also, development dataset provided by the organizer is used for development and model evaluation. ## conclusion this paper describes university of tsukuba's submission to iwslt20 open domain translation task. we trained standard transformer models and adopted various techniques for better performance, including data filtering, large-scale noised training, model ensemble, reranking and postprocessing. we demonstrated the effectiveness of our approach and achieved 33.0 bleu scores for @xmath1.3 bleu scores for @xmath2.