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arXiv:2609.22793v1 Announce Type: new Abstract: LLM-based machine translation evaluation can closely match human judgments, but in practice it remains largely diagnostic, with the signals rarely tran...
arXiv:2609.16340v1 Announce Type: cross Abstract: Machine translation systems are periodically upgraded to stronger models, but the available preference signal is human post-edits of an older system'...
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 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.
The paper introduces ProNMT, a reward-guided iterative self‑training approach that balances global translation quality with pronoun‑specific feedback for context‑aware machine translation. ProNMT samples candidate translations, scores them using reference‑free quality estimation and a pronoun label derived from references, and fine‑tunes on the highest‑scoring candidate. Experiments on English–German Europarl and English–French News Commentary show that ProNMT outperforms standard context‑aware fine‑tuning on BLEU and COMET, while ablations reveal that pronoun‑only feedback can harm overall quality and that confidence‑weighted feedback outperforms hard binary feedback.
As state-of-the-art machine translation models saturate standard benchmarks, the field needs more challenging evaluations to distinguish between models of varying quality. We propose augmenting existi...