用MBR解码从NMT和LLM中选最优译文，华为提交结果表现优异。
Choose the Final Translation from NMT and LLM hypotheses Using MBR Decoding: HW-TSC's Submission to the WMT24 General MT Shared Task
- 融合NMT与LLM生成多个译文，通过MBR解码择优
- 在WMT24英中翻译任务中取得竞争力结果
- 适合关注大模型与翻译融合的实践者参考
本文介绍华为翻译服务研究中心（HW-TSC）参加WMT24通用机器翻译共享任务的提交方案，针对英中（en2zh）语言对。延续往年做法，基于深度Transformer-big架构，采用正则化丢弃、双向训练、数据多样化、前向翻译、反向翻译、交替训练、课程学习及归纳集成学习等训练策略训练神经机器翻译（NMT）模型。不同之处在于，还对基于大语言模型（LLM）的翻译模型进行了持续预训练、监督微调与对比偏好优化。通过最小贝叶斯风险（MBR）解码，从NMT与基于LLM的MT模型生成的多个假设中选择最终译文，提交结果在最终评估中表现具有竞争力。
原文摘要 · Abstract (English)
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.
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