Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies
This work addresses the challenge of high-quality real-time translation for applications like live interpretation, though it appears incremental as it builds on existing LLM frameworks with new actions.
The paper tackled the problem of Simultaneous Machine Translation (SiMT) by extending the action space with adaptive actions like Sentence_Cut and Drop, enabling real-time restructuring and omission while preserving semantics, and experiments on benchmarks showed consistent improvements in semantic metrics and lower delay compared to baselines.
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.