The paper introduces a new automatic evaluation metric for human simultaneous interpreting that aligns with traditional analytic rubrics. A corpus of 1,101 professionally scored SI segments is created, covering meaning transfer, delivery quality, and perceived latency. Using a LoRA‑adapted COMET‑KIWI encoder with dual regression heads, the model achieves Pearson correlations of 0.388 for meaning transfer and 0.301 for delivery quality, outperforming the frozen baseline while acknowledging low rater agreement.
By Ziyu Zhang, Satoshi Nakamura
EviSI is an evidence‑based evaluation agent for low‑latency simultaneous speech‑to‑speech translation. It combines Multidimensional Quality Metrics with interpreter‑developed criteria, using shared source evidence to assess four dimensions—Anchor, Event, Logic, and Fluency—while deduplicating verified errors before scoring. On English‑to‑Chinese and Chinese‑to‑English data, EviSI’s rankings correlate strongly with human judgments, outperforming BLEU and COMET, and its multilingual extension maintains these correlations across five language directions.
By Ben Yan, Zongyao Li, Xiaoyu Chen, Daimeng Wei, Weidong Liu, Huan Zhao, Chong Li, Yaode Wang, Yuzhe Shang
arXiv:2609.22778v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) are increasingly used as evaluators, yet their reliability in professional assessment tasks that require exper...
By Yuhan Lu, Yi Yao, Hua Shen, Katie Aafjes-van Doorn, Zhaonan Wang
arXiv:2601. 18253v2 Announce Type: replace-cross Abstract: Accurate evaluation of user satisfaction is critical for iterative development of conversational AI.
By Peng Sun, Xiangyu Zhang, Duan Wu, Lu Tan, Jian Lin, He Yang, Qi Qian, Yikai Wang
EviSI is a large language model evaluation agent designed for simultaneous speech-to-speech translation. It adapts Multidimensional Quality Metrics to assess semantic fidelity and oral expression, using shared source evidence and deterministic scoring. In English‑to‑Chinese, EviSI achieves a mean Kendall agreement of 0.707 with human system rankings, outperforming baseline metrics, and shows positive concordance with COMET across five translation directions.
arXiv:2601.08654v3 Announce Type: replace
Abstract: Rubric-based text evaluation increasingly relies on large language models (LLMs) as scalable judges, yet frozen black-box models can interpret the...
By Yihan Hong, Huaiyuan Yao, Bolin Shen, Wanpeng Xu, Hua Wei, Yushun Dong