{"id":1187175,"date":"2026-09-23T15:24:29","date_gmt":"2026-09-23T22:24:29","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/dynamic-lagging-for-simultaneous-translation\/"},"modified":"2026-09-23T15:32:55","modified_gmt":"2026-09-23T22:32:55","slug":"dynamic-lagging-for-simultaneous-translation","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/dynamic-lagging-for-simultaneous-translation\/","title":{"rendered":"Dynamic Lagging for Simultaneous Translation"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">In cascaded simultaneous speech translation, the machine translation (MT) system cannot control the read&#8211;write schedule of the upstream recognizer: it must decide, from a growing source prefix, how much target text to commit. We make a sentence-trained, decoder-only LLM prefix-aware by fine-tuning it on stable prefixes&#8212;the longest prefix that any translation up to the current partial source has shared with the model&#8217;s own full-source output&#8212;mixed with full-sentence pairs, and prompt it through a single force-decode turn that carries the committed target forward as more source arrives, making the system flicker-free by construction. We fine-tune Qwen3-8B for EN to DE, JA, ZH, simulating the source stream with reference-transcript prefixes. Prefix finetuning preserves full-sentence quality while improving worst-position chunk quality, and it improves calibration of token-level commit confidence, reducing expected calibration error (ECE) on early source prefixes against a stable-prefix oracle. A single training-free threshold on that confidence is the most effective of the three latency controls we compare: it traces a continuous quality&#8211;latency frontier that outperforms the discrete wait-k and target-suffix-deletion quality-latency tradeoff mechanisms. The effect holds well on FLEURS, WMT24++, and CoVoST~2 test sets, under both COMET and MetricX.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In cascaded simultaneous speech translation, the machine translation (MT) system cannot control the read&#8211;write schedule of the upstream recognizer: it must decide, from a growing source prefix, how much target text to commit. We make a sentence-trained, decoder-only LLM prefix-aware by fine-tuning it on stable prefixes&#8212;the longest prefix that any translation up to the current [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Hieu Hoang","user_id":0},{"type":"text","value":"Amittai 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