The paper introduces Hikari, a policy‑free, end‑to‑end model that performs simultaneous speech‑to‑text translation and streaming transcription. It employs a Decoder Time Dilation mechanism to mitigate overuse of WAIT tokens during training and a supervised fine‑tuning strategy that helps the model recover from delays, improving the quality‑latency trade‑off. Despite its modest size, Hikari achieves competitive translation quality at consistently low latency, outperforming larger published IWSLT 2026 submissions and proprietary API systems on en‑ja, en‑de, and en‑ru tasks.
By Roman Koshkin, Jeon Haesung, Lianbo Liu, Hao Shi, Mengjie Zhao, Yusuke Fujita, Yui Sudo
arXiv:2504.11809v2 Announce Type: replace
Abstract: Simultaneous speech translation (SimulST) produces translations incrementally while processing partial speech input. Although large language models...
By Biao Fu, Donglei Yu, Minpeng Liao, Chengxi Li, Xinjie Chen, Yidong Chen, Kai Fan, Xiaodong Shi
arXiv:2609.00588v1 Announce Type: new
Abstract: Reranking methods, such as Minimum Bayes Risk (MBR) decoding and Quality Estimation (QE) reranking, are widely used in modern neural machine translatio...
By Guangyu Chen, Boxuan Lyu, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura
arXiv:2609.18720v1 Announce Type: new
Abstract: Learned quality estimation (QE) models such as COMETKiwi are widespread and work well for general machine translation evaluation. However, they are kno...
By Kathy H\"ammerl, Gabriel Bretschner, Joern Wuebker
arXiv:2608. 15932v1 Announce Type: new Abstract: As state-of-the-art machine translation models saturate standard benchmarks, the field needs more challenging evaluations to distinguish between models of varying quality.
By William Kalikman, \v{S}imon Sukup, Michal Te\v{s}nar, Vil\'em Zouhar
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...
The paper examines two test‑time scaling methods for large language models in machine translation: sequential sampling, where later attempts build on earlier ones, and parallel sampling, such as independent i.i.d. sampling with reranking. Sequential sampling shows a higher performance ceiling, offering a more diverse and effective set of translations, especially with limited sampling budgets. Human analysis reveals that while sequential sampling improves fluency and naturalness, it can reduce accuracy when the inference budget is large, and the authors attribute this effect to the model’s access to a larger target‑side context.
By Di Wu, Sergey Troshin, Christof Monz, Antske Fokkens, Vlad Niculae
EnSiTa is a trilingual multi‑domain parallel dataset and benchmark for English, Sinhala, and Tamil. It contains human post‑edited training data across seven domains and professionally translated test sets for those domains plus an additional one, all produced through a multi‑year, rigorously quality‑controlled process. The authors use EnSiTa to conduct a comprehensive study of domain‑specific machine translation across six language directions, comparing from‑scratch Transformers, pre‑trained models, and decoder‑only LLMs under various training‑data sizes, model scales, and domain settings.
By Surangika Ranathunga, Nisansa de Silva, Aloka Fernando, Kavindu Warnakulasuriya, Isuru Wijesiri, Menan Velayuthan, Charitha Rathnayaka, Thivaharan Varatharajan, Sajeevi Silva, Piumi Kandanaarachchi, Uthayasanker Thayasivam
The paper investigates weight‑space merging of independently fine‑tuned multilingual machine translation models. Experiments show that merging is more successful when models share a target language, yet it still cannot match the peak performance of language‑specific checkpoints. When target languages differ, performance drops sharply, and analysis reveals that overlapping neuron activation and incompatible upper‑layer geometries cause these failures.
By Baban Gain, Trilok Nath Singh, Asif Ekbal
arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.
By Tom\'a\v{s} Burkert, Angelika Peljak-{\L}api\'nska, David Zelen\'y
arXiv:2509.17930v3 Announce Type: replace-cross
Abstract: Multilingual translation suffers from computational redundancy, especially when translating into multiple languages simultaneously. In additi...
By Yiwen Guan, Jacob Whitehill
Doc2FRC introduces Fixed-Range Chunking (FRC), a dynamic programming method that partitions documents into chunks of a predefined length interval, ensuring consistent length distributions during training and inference. This approach reduces train-test length mismatch, mitigates n-gram repetition, and improves translation quality for 7B LLMs compared to direct Doc2Doc fine-tuning. Experiments on IWSLT2017 and a new 10-language test set, GlobVDoc, demonstrate that FRC outperforms existing document-level machine translation methods and enhances out-of-distribution translation performance.
By Xiaotian Wang, Youyuan Lin, Zhan Shen, Hitomi Yanaka