HW - TSC ’s Participation in the WAT 2020 Indic Languages Multilingual Task (2020)

Abstract: This paper describes our work in the WAT 2020 Indic Multilingual Translation Task. We participated in all 7 language pairs (En<->Bn/Hi/Gu/Ml/Mr/Ta/Te) in both directions under the constrained condition—using only the officially provided data. Using transformer as a baseline, our Multi->En and En->Multi translation systems achieve the best performances. Detailed data filtering and data domain selection are the keys to performance enhancement in our experiment, with an average improvement of 2.6 BLEU scores for each language pair in the En->Multi system and an average improvement of 4.6 BLEU scores regarding the Multi->En. In addition, we employed language independent adapter to further improve the system performances. Our submission obtains competitive results in the final evaluation.

## introduction this paper describes our work in the wat 2020 indic multilingual translation task @xcite . our team (team id: hw-tsc) participated in all seven language pairs (en↔bn/hi/gu/ml/mr/ta/te) by training @xmath0. based on previous works, we mainly focus on exploiting fine-grained data filtering and domain data selection techniques to enhance system performance. multistep filtering is conducted to sort out the high-quality subset for training. several other strategies, including back-translation @xcite , tagged back-translation @xcite , joint training @xcite , fine-tuning @xcite , ensemble and adapter fine-tuning @xcite are employed and tested in our experiments. sacrebleu @xcite is used to evaluation the system performance. ## data this section describes the size and source of the dataset as well as our data filtering techniques. ## result this section presents the experimental results for each direction of all three language pairs in ## analysis here are several findings worthy of sharing during our experiments: ## conclusion this paper presents the submissions by hw-tsc on the wat 2020 indic multilingual translation task. we perform experiments with a series of pre-processing and training strategies. the effectiveness of each strategy is demonstrated. our submission finally achieves competitive result in the evaluation.