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The paper introduces a training strategy for cascaded simultaneous speech translation that allows the system to dynamically decide how much of the source prefix to translate. By fine‑tuning a large language model (Qwen3‑8B) on stable prefixes—pairs of source prefixes and the longest shared translation with the full sentence—the authors enable contextual read‑write decisions beyond fixed wait‑k or target‑suffix deletion. Experiments on English‑to‑German, Japanese, and Chinese demonstrate that stable prefixes improve the quality‑latency tradeoff across various test sets.
The paper reports UvA-MT’s submission to the WMT 2026 General Machine Translation shared task, competing in the unconstrained track for all 23 translation directions. The approach leverages test‑time methods of Large Language Models, using sequential sampling with a refinement prompt to generate multiple translation candidates and then employing LLMs as pairwise judges in a round‑robin voting scheme to select the best candidate. Offline evaluation on the WMT25 benchmark shows a clear improvement over traditional point‑wise best‑of‑N selection.
arXiv:2512. 07540v4 Announce Type: replace-cross Abstract: Error Span Detection (ESD) extends automatic machine translation (MT) evaluation by localizing translation errors and labeling their severity.
arXiv:2610.07545v1 Announce Type: new Abstract: We present a fully offline speech-to-speech translation pipeline that runs on a Jetson Nano (4 GB) and corrects its own weak translations without retra...
arXiv:2605.28042v2 Announce Type: replace-cross Abstract: Modern large language models (LLMs) achieve state-of-the-art machine translation performance, but they do so as broad generalists largely tra...
The paper introduces the Last Translation Benchmark (LTB), a live dataset of human-authored and peer‑reviewed examples—including texts, images, audio, and videos—that are designed to break current state‑of‑the‑art machine translation models. Each example is accompanied by handcrafted verification rules that specify concrete failure cases, providing a reliable and actionable evaluation method. The benchmark aims to overcome the limitations of existing automatic metrics and gold human evaluations, which often lack reproducibility, objectivity, and scalability.