Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies

• URL: http://arxiv.org/abs/2601.11002v1
• Date: Fri, 16 Jan 2026 05:26:16 GMT
• Title: Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies
• Authors: Qianen Zhang, Zeyu Yang, Satoshi Nakamura,
• Abstract summary: Simultaneous Machine Translation (SiMT) requires high-quality translations under strict real-time constraints.<n>We extend the action space of SiMT with four adaptive actions: Sentence_Cut, Drop, Partial_Summarization and Pronominalization.<n>We adapt these actions in a large language model (LLM) framework and construct training references through action-aware prompting.
• Score: 6.010207559477024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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