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

• URL: http://arxiv.org/abs/2509.21801v1
• Date: Fri, 26 Sep 2025 02:57:36 GMT
• Title: Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies
• Authors: Qianen Zhang, 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_MARIZATION and PRONOMINALIZATION.<n>We implement these actions in a decoder-only large language model (LLM) framework and construct training references through action-aware prompting.
• Score: 4.487634497356904
• 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 encoder-decoder 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 implement these actions in a decoder-only large language model (LLM) framework and construct training references through action-aware prompting. To evaluate both quality and latency, we further develop a latency-aware TTS pipeline that maps textual outputs to speech with realistic timing. Experiments on the ACL60/60 English-Chinese and English-German benchmarks show that our framework consistently improves semantic metrics (e.g., COMET-KIWI) and achieves lower delay (measured by Average Lagging) compared to reference translations and salami-based baselines. Notably, combining DROP and SENTENCE_CUT yields the best overall 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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