Abstract
This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT24 general machine translation (MT) shared task, where we participate in the English to Chinese (en2zh) language pair. Similar to previous years' work, we use training strategies such as regularized dropout, bidirectional training, data diversification, forward translation, back translation, alternated training, curriculum learning, and transductive ensemble learning to train the neural machine translation (NMT) model based on the deep Transformer-big architecture. The difference is that we also use continue pre-training, supervised fine-tuning, and contrastive preference optimization to train the large language model (LLM) based MT model. By using Minimum Bayesian risk (MBR) decoding to select the final translation from multiple hypotheses for NMT and LLM-based MT models, our submission receives competitive results in the final evaluation.
Abstract (translated)
本文介绍了华为翻译服务中心（HW-TSC）参与MT24通用机器翻译（MT）共享任务，我们参加英语到中文（en2zh）语言对。与以往的工作类似，我们使用如正则化 dropout、双向训练、数据多样化、前向翻译、反向翻译、交替训练、课程学习以及转换器模型（NMT）等训练策略来训练基于深度Transformer架构的神经机器翻译（NMT）模型。不同之处在于，我们还使用了继续预训练、有监督的微调以及对比偏好优化来训练基于MT的大语言模型（LLM）模型。通过使用最小贝叶斯风险（MBR）解码来选择NMT和LLM-based MT模型的最终翻译，我们在最终评估中获得了竞争力的结果。
URL
https://arxiv.org/abs/2409.14800
