arXiv Machine Learning
Aug 5

M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models

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 Computation and Language
Sep 4

Benchmarking Machine Translation on Chinese Social Media Texts

The paper introduces CSM-MTBench, a benchmark for evaluating machine translation on Chinese social media text. It addresses two main challenges: limited parallel data due to slang and stylistic nuances, and inadequate evaluation metrics that miss these informal features. The benchmark includes two expert-curated subsets—Fun Posts and Social Snippets—and proposes specialized evaluation methods for each, revealing significant differences among over 20 MT models in handling semantic and stylistic aspects.

By Kaiyan Zhao, Zheyong Xie, Zhongtao Miao, Xinze Lyu, Yao Hu, Shaosheng Cao
arXiv Computation and Language
2d ago

EviSI: An Evidence-Based Evaluation Agent for Simultaneous Interpreting

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