Improving Neural Machine Translation Formality Control with Domain Adaptation and Reranking-based Transductive Learning (2023)

Abstract: This paper presents Huawei Translation Service Center (HW-TSC)’s submission on the IWSLT 2023 formality control task, which provides two training scenarios: supervised and zero-shot, each containing two language pairs, and sets constrained and unconstrained conditions. We train the formality control models for these four language pairs under these two conditions respectively, and submit the corresponding translation results. Our efforts are divided into two fronts: enhancing general translation quality and improving formality control capability. According to the different requirements of the formality control task, we use a multi-stage pre-training method to train a bilingual or multilingual neural machine translation (NMT) model as the basic model, which can improve the general translation quality of the base model to a relatively high level. Then, under the premise of affecting the general translation quality of the basic model as little as possible, we adopt domain adaptation and reranking-based transductive learning methods to improve the formality control capability of the model.

## introduction machine translation (mt) @xcite ) models typically return one single translation for each input sentence. this means that when the input sentence is ambiguous, the mt model must choose a translation from among various valid options, without regard to the intended use case or target audience. therefore, there is a need to control certain attributes @xcite of the text generated in a target language such as politeness @xcite or formality @xcite @xcite . the lack of gold translation with alternate formality for supervised training and evaluation has lead researchers to rely on synthetic supervision training and manual evaluation in past work @xcite . fortunately, the iwslt formality control task now provides a new benchmark foot_0 @xcite by contributing high-quality training datasets and test datasets for multiple language pairs. this paper presents hw-tsc's submission on the iwslt 2023 formality control task. how formality distinctions are expressed grammatically and lexically can vary widely by language. thus, we participate in the formality control task of all these four language pairs to investigate a general formality control method that can be applied to different language pair. in addition, we also investigate the difference in formality control between constrained and unconstrained conditions by introducing the mbart model @xcite ) under unconstrained condition. ## method in our implementation, we first use a multi-stage pre-training method to train a general nmt model with relatively high translation quality. then, we use domain adaptation method to fine-tune the nmt model so that the model can have basic formality control capability. finally, we use the reranking-based transductive learning (rtl) method to further improve the formality control capability of the model. ## conclusions this paper presents hw-tsc's submission on the iwslt 2023 formality control task, in which we participate in both constrained and unconstrained tasks for all four language pairs. for the formality control task, we use a multi-stage pre-training method to improve the general translation quality of the basic model. we also adopt domain adaptation and rtl methods to improve the model's formality control capability. experimental results show that these methods we have adopted are extremely effective, but how to improve general translation quality more effectively and achieve formality control with less training resources is still worthy of further research.