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            <title>Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026)</title>
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        <name type="personal">
            <namePart type="given">Elizabeth</namePart>
            <namePart type="family">Salesky</namePart>
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            <namePart type="given">Matteo</namePart>
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    <abstract>We present the CUHKSZ Team submission to the IWSLT 2026 Simultaneous Speech Translation evaluation, targeting the main and Extra Context tracks for English→Chinese, German on unsegmented speech. Our system is built upon Qwen3-Omni-30B-A3B, a natively aligned audio-text LLM. Under the Constrained condition, we apply LoRA adaptation exclusively to the LLM. Specifically, we construct syntax-aware, chunk-aligned supervision from existing ASR corpora, using Qwen3-30B-Instruct to synthesize target translations. This enables the model to internalize the simultaneous read/write policy by autonomously predicting \ensuremath&lt;wait\ensuremath&gt; tokens at semantically incomplete boundaries. With the policy internalized, execution is delegated to a lightweight streaming agent served via vLLM. This agent feeds audio in fixed chunks, manages a bounded dialogue history, and enforces strict emission controls to minimize computation-aware delay. For the sub-track, contextual priors are dynamically injected into the prompt. On the official dev set, our 0–2 s latency regime submissions achieve 40.5 BLEU (1.95 s) for En→Zh and 27.7 BLEU (1.72 s) for En→De. In the 2–4 s regime, performance scales to 42.1 BLEU (2.16 s) and 30.5 BLEU (2.29 s) respectively.</abstract>
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%0 Conference Proceedings
%T CUHKSZ Simultaneous Speech Translation System for IWSLT 2026
%A Yang, Zeyu
%A Nakamura, Satoshi
%Y Salesky, Elizabeth
%Y Anastasopoulos, Antonios
%Y Negri, Matteo
%Y Federico, Marcello
%S Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, USA (in-person and online)
%@ 979-8-89176-411-8
%F yang-nakamura-2026-cuhksz
%X We present the CUHKSZ Team submission to the IWSLT 2026 Simultaneous Speech Translation evaluation, targeting the main and Extra Context tracks for English→Chinese, German on unsegmented speech. Our system is built upon Qwen3-Omni-30B-A3B, a natively aligned audio-text LLM. Under the Constrained condition, we apply LoRA adaptation exclusively to the LLM. Specifically, we construct syntax-aware, chunk-aligned supervision from existing ASR corpora, using Qwen3-30B-Instruct to synthesize target translations. This enables the model to internalize the simultaneous read/write policy by autonomously predicting \ensuremath<wait\ensuremath> tokens at semantically incomplete boundaries. With the policy internalized, execution is delegated to a lightweight streaming agent served via vLLM. This agent feeds audio in fixed chunks, manages a bounded dialogue history, and enforces strict emission controls to minimize computation-aware delay. For the sub-track, contextual priors are dynamically injected into the prompt. On the official dev set, our 0–2 s latency regime submissions achieve 40.5 BLEU (1.95 s) for En→Zh and 27.7 BLEU (1.72 s) for En→De. In the 2–4 s regime, performance scales to 42.1 BLEU (2.16 s) and 30.5 BLEU (2.29 s) respectively.
%U https://aclanthology.org/2026.iwslt-1.13/
%P 111-118

Markdown (Informal)

[CUHKSZ Simultaneous Speech Translation System for IWSLT 2026](https://aclanthology.org/2026.iwslt-1.13/) (Yang & Nakamura, IWSLT 2026)

• CUHKSZ Simultaneous Speech Translation System for IWSLT 2026 (Yang & Nakamura, IWSLT 2026)

ACL

• Zeyu Yang and Satoshi Nakamura. 2026. CUHKSZ Simultaneous Speech Translation System for IWSLT 2026. In Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026), pages 111–118, San Diego, USA (in-person and online). Association for Computational Linguistics.