HW - TSC ’s Submissions to the WMT 23 Discourse-Level Literary Translation Shared Task (2023)

Abstract: This paper introduces HW-TSC’s submission to the WMT23 Discourse-Level Literary Translation shared task. We use standard sentence-level transformer as a baseline, and perform domain adaptation and discourse modeling to enhance discourse-level capabilities. Regarding domain adaptation, we employ Back-Translation, Forward-Translation and Data Diversification. For discourse modeling, we apply strategies such as Multi-resolutional Document-to-Document Translation and TrAining Data Augmentation.

## introduction transformer architectures @xcite have achieved outstanding performance on sentence-level machine translation tasks, but still have some shortcomings when it comes to discourse-level machine translation. particularly, for machine translation scenarios that are highly discourse-dependent, such as novel translation and conversation translation, the performance is unsatisfactory. this paper presents the submission of hw-tsc to the wmt23 discourse-level literary translation shared task. we utilize an effective data cleaning pipeline summarized in our previous works @xcite @xcite to process the training data. we employ regularized dropout, forward translation, back translation, data diversification to train a strong baseline. on top of the baseline, we apply strategies including multi-resolutional doc2doc translation (mr-doc2doc), training data augmentation (tada) to enhance discourse-level translation capabilities. the general translation model does not work well in novel translation. we found that the biggest factor affecting the quality of translation is domain adaptation; however, domain adaptation cannot solve the consistency of named entity such as names, addresses, and zero pronoun in novel translation. the consistency needs to be optimized by using strategies such as mr-doc2doc and tada. ## conclusion it was believed that algorithm enhancement can make the model handle long inputs so that discourse-level translation would be improved. however, it can only achieve slight improvement. it is more important to achieve domain adaptation first for the sentence-level model. the discourselevel translation strategy can get the best performance based on domain adaptation.