HW - TSC Translation Systems for the WMT 22 Chat Translation Task (2022)

Abstract: This paper describes the submissions of Huawei Translation Services Center (HW-TSC) to WMT22 chat translation shared task on English-Germany (en-de) bidirection with results of zore-shot and few-shot tracks. We use the deep transformer architecture with a lager parameter size. Our submissions to the WMT21 News Translation task are used as the baselines. We adopt strategies such as back translation, forward translation, domain transfer, data selection, and noisy forward translation in task, and achieve competitive results on the development set. We also test the effectiveness of document translation on chat tasks. Due to the lack of chat data, the results on the development set show that it is not as effective as sentence-level translation models.

## introduction neural machine translation (nmt) @xcite @xcite has achieved good translation results in most scenarios, but few researches have been done in the field of chat translation, mainly because of insufficient chat data. wmt20 holds the chat translation shared task @xcite for the first time. the data set mainly includes pre-sales conversations between customers and agents (meal booking, air ticket reservation, etc.). this year, the data set focuses on post-sales conversations between customers and agents. although the translation content is all about chat, the domains are slightly different. the results show that the data from previous years can not effectively improve the quality of the model for this year's task. we participate in the en-de bidirectional translation task. the en-de bidirectional models we submitted to the wmt21 news task @xcite are used as the baseline models and the architecture is deep transformer @xcite . commonly-used optimization strategies are used, such as domain transfer, data selection, back translation, self-training, noisy selftraining, finetuning and model averaging. considering that the chat task is a context-aware translation task, we conduct a series of documentlevel @xcite experiments using wmt document data, but it does not work well on development sets. the analysis shows that the document data deviates greatly from the chat domain, and the data therefore cannot effectively improve chat translation quality. according to the results, the best models are obtained by selecting in-domain data from out domain data by the development sets. this paper is structured as follows: section 2 describes our data volume and data pre-processing method. the model structure and method we used are presented in section 3. section 4 details our experiment setting. we present the results in section 5, and finally we conclude our work in section 6. ## experiment setting during the training phase, we use pytorch-based fairseq 3 @xcite open-source framework as our benchmark system. each model is trained using 8 gpus with a batch size of 2048. the update frequency is 4 and the learning rate is 5e-4. the label smoothing rate is set to 0.1, the warm-up steps to 4000, and the dropout to 0.3. adam optimizer (kingma and ba, 2015) with β1=0.9 and β2=0.98 is also used. in the evaluation phase, we use marian 4 @xcite for decoding and then calculate the sacrebleu scores on the wmt22 chat translation task dev sets to measure the performance of each model. ## result and analysis finally, we select the result of data selection after model averaging as the primary submission, noisy self-training after model averaging as the con-trastive2. note that, the two submissions before are few-shot. the result of the baseline model as the submission of the zero-shot track, is contrastive1. ## conclusion this paper presents the submissions of hw-tsc to the wmt 2022 chat translation shared task. for both direction in customer-agent translation task, we perform experiments with a series of preprocessing and training strategies. the results show that bilingual data selection achieves the best results. in the future, we will continue to explore the applicability of bilingual data selection mentioned in this paper. besides, the performance of document-level translation model is limited given the amount of data. it has not achieved the expected results on this task, and we will continue to explore the impact of context for the chat task.