Treating General MT Shared Task as a Multi-Domain Adaptation Problem: HW - TSC ’s Submission to the WMT 23 General MT Shared Task (2023)

Abstract: This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT23 general machine translation (MT) shared task, in which we participate in Chinese↔English (zh↔en) language pair. We use Transformer architecture and obtain the best performance via a variant with larger parameter size. We perform fine-grained pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. We mainly use model enhancement strategies, including Regularized Dropout, Bidirectional Training, Data Diversification, Forward Translation, Back Translation, Alternated Training, Curriculum Learning and Transductive Ensemble Learning. Our submissions obtain competitive results in the final evaluation.

## introduction machine translation (mt) @xcite refers to the automatic translation of text from one language to another, while the wmt23 general mt shared task focuses on evaluation of general mt capabilities. compared with the news shared task in previous years, the general mt shared task involves multiple domains. the testsets contain data in news, user generated (social), conversational, and ecommerce domains. this paper presents the submission of hw-tsc to the wmt23 general mt shared task, in which we participate in zh↔en language pair. our method is mainly based on previous works @xcite @xcite . we perform multi-step data cleansing on the provided dataset and only keep a high-quality subset for training. at the same time, several model enhancement strategies are tested in a pipeline, including regularized dropout @xcite , bidirectional training @xcite , data diversification @xcite , forward translation @xcite , back translation @xcite , alternated training @xcite , curriculum learning @xcite and transductive ensemble learning @xcite . our system report includes four parts. section 2 focuses on our data processing strategies while section 3 describes our training details. section 4 explains our experiment settings and training processes and section 5 presents the results. ## experiment settings we use the open-source fairseq @xcite for training, then we use sacrebleu (post, 2018) foot_1 and wmt20-comet-da model @xcite to measure system performances. the main parameters are as follows: each model is trained using 8 a100 gpus, batch size is 6144, parameter update frequency is 2, and learning rate is 5e-4. the number of warmup steps is 4000, and model is saved every 1000 steps. the architecture we used is described in section 3.1. we adopt dropout, and the rate varies across different training phases. r-drop is used in model training, and we set λ to 5. ## results regarding zh↔en, we use regularized dropout, bidirectional training, data diversification, forward translation, back translation, alternated training, curriculum learning, and transductive ensemble learning. the evaluation results of @xmath0. ## official automatic evaluation results in our final submission, we add post-processing for punctuation correction and entity preservation. wmt @xcite present an automatic evaluation of the systems submitted to the general machine translation task, including the following three different automatic metrics: chrf, bleu and comet. we rank the systems according to comet scores, and unconstrained systems are in a grey background in the tables. ## conclusion this paper presents the submission of hw-tsc to the wmt23 general mt task. we participate in zh↔en language pair and perform experiments with a series of pre-processing and training strategies. the effectiveness of each strategy is demonstrated. our experiments show that our model training strategies are effective. our submission finally achieve competitive results in the evaluation.