给大模型翻译加新动作，让机器同传更像人。
Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies
- 用四个新操作（切句、删减、简写、代词化）重构实时翻译流程
- 在多个语言对上提升语义质量并降低延迟，效果优于传统方法
- 适合研究实时翻译或人机交互的开发者参考
同步机器翻译（SiMT）需在严格实时约束下产出高质量译文，传统仅含读/写动作的策略难以满足。本文扩展动作空间，引入四种自适应操作：断句（Sentence_Cut）、删减（Drop）、部分摘要（Partial_Summarization）和代词化（Pronominalization），实现实时内容重构、省略与简化，同时保持语义一致。在大语言模型框架中集成这些动作，并通过感知动作提示构建训练样本。为评估译文质量与字级单调性，进一步开发了时延感知的文本转语音（TTS）流水线，使输出语音具备真实时间特征。在ACL60/60英语-中文、英语-德语及英语-日语基准上实验表明，本框架在语义指标上持续优于参考译文与萨拉米基线，且延迟更低。尤其当结合使用删减与断句时，流畅性与延迟的平衡显著提升。结果表明，丰富基于大模型的同步翻译动作空间，是缩小机器与人类口译差距的可行方向。
原文摘要 · Abstract (English)
Simultaneous Machine Translation (SiMT) requires high-quality translations under strict real-time constraints, which traditional policies with only READ/WRITE actions cannot fully address. We extend the action space of SiMT with four adaptive actions: Sentence_Cut, Drop, Partial_Summarization and Pronominalization, which enable real-time restructuring, omission, and simplification while preserving semantic fidelity. We adapt these actions in a large language model (LLM) framework and construct training references through action-aware prompting. To evaluate both quality and word-level monotonicity, we further develop a latency-aware TTS pipeline that maps textual outputs to speech with realistic timing. Experiments on the ACL60/60 English-Chinese, English-German and English-Japanese benchmarks show that our framework consistently improves semantic metrics and achieves lower delay compared to reference translations and salami-based baselines. Notably, combining Drop and Sentence_Cut leads to consistent improvements in the balance between fluency and latency. These results demonstrate that enriching the action space of LLM-based SiMT provides a promising direction for bridging the gap between human and machine interpretation.
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