Tohoku- AIP - NTT at WMT 2020 News Translation Task (2020)

Abstract: In this paper, we describe the submission of Tohoku-AIP-NTT to the WMT’20 news translation task. We participated in this task in two language pairs and four language directions: English <–> German and English <–> Japanese. Our system consists of techniques such as back-translation and fine-tuning, which are already widely adopted in translation tasks. We attempted to develop new methods for both synthetic data filtering and reranking. However, the methods turned out to be ineffective, and they provided us with no significant improvement over the baseline. We analyze these negative results to provide insights for future studies.

## introduction the joint team of tohoku university, riken aip, and ntt (tohoku-aip-ntt) participated in the wmt'20 shared news translation task in two language pairs and four language directions: @xmath0, @xmath1, @xmath2, and @xmath3. at the very beginning of this year's shared task, we planned to employ the following two enhancements at the core of our system. the first enhancement is the noisy synthetic data filtering @xcite to better utilize the millions of backtranslated synthetic data. however, as we analyze in section 5.1, this filtering turned out to be ineffective. the second enhancement is the reranking of n-best candidates generated a the model. given a collection of scores from multiple generative/translation models, our reranking module selects the best candidate. we attempted to develop sophisticated machine learning based methods for optimizing the weight of each score. however, we found that those methods are not as effective as the simple grid search on the bleu score (details in section 3.7 and section 5.3). eventually, we designed our system as a combination of techniques that are already widely adopted in the shared task, such as back-translation and fine-tuning. the overview of our system is shown in figure 1 . we achieved the first place in @xmath4. ## results performance on the validation set we show the validation performance of our system in we used the following models for postensem-ble of @xmath6: (1) model (f) ( the performance of @xmath7; this is because ocelot computes the bleu score with character-level segmentation, whereas we used the mecab-based word-level segmentation foot_7 . we also computed the bleu score with the mecab-based segmentation for reference and obtained 25.8 points. ## analysis in this section, we introduce several negative results from our preliminary experiments. our attempts include the following: (1) filtering synthetic data, (2) incorporating forward-translation, and (3) developing a more sophisticated reranking method. we also analyze the issue regarding the use of brackets in the @xmath8. ## conclusion in this paper, we described the submission of the joint team of tohoku, aip, and ntt (tohoku-aip-ntt) to the wmt'20 news translation task. we participated in the en↔de and en↔ja translation. in preliminary experiments, we attempted new techniques such as synthetic data filtering, forward-translation, and sophisticated reranking. however, none of them was effective. in the submission, we used several standard techniques such as back-translation and fine-tuning. as a result, we achieved the best bleu score on @xmath9.