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:2607. 20241v1 Announce Type: cross Abstract: Culturally loaded translation poses unique challenges for machine translation (MT), as meanings are deeply embedded in socio-cultural contexts beyond surface linguistic forms.
By Yiming Wang, Jiayuan Di
arXiv:2601. 16217v2 Announce Type: replace-cross Abstract: Large language models increasingly mediate multilingual professional communication, where useful generation requires adapting to community conventions about which expressions are retained, translated, or mixed.
By Qingyan Yang, Tongxi Wang, Yunsheng Luo
The paper introduces a three‑layer checklist-and-judge framework to evaluate interpreter agents that mediate live conversation across languages. It assesses semantic, pragmatic, and cultural‑social dimensions—naturalness, intent, and social appropriateness—rather than just fidelity, in both single‑turn and multi‑turn settings. Extensive validation shows that conventional MT metrics miss failures in stronger interpreters, and that context, structured instructions, and cultural cues influence communicative success.
By Faiz Ghifari Haznitrama, Alice Oh
The paper introduces Retrieval-Enhanced Cultural Evaluation (CUE-MT), a framework that trains social media translation models by incorporating a calibrated, domain‑specific Judger and an Agent that retrieves external evidence to generate concise hints for scoring translations. Using this approach, CUE-MT outperforms comparable open‑source baselines across multiple model sizes on both CUE‑MT‑Bench and three public benchmarks, achieving cultural effectiveness gains of 7.44–15.12 percentage points in human evaluation. Retrieval notably boosts the Judger’s four‑way classification accuracy by 8.07 percentage points compared to a no‑retrieval baseline, and ablation studies confirm the benefits of hard‑example mining and Agent‑enhanced rewards.
By Linjuan Wu, Yiwen Qiu, Xinze Lyu, Dingming Li, Hui Yao, Zhongyi Sang, Ye Guo, Daoxin Zhang, Zhe Xu, Fei zhao, Mu Chuan, Yao Hu, Weiming Lu, Yongliang Shen
Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large language models remain limited. We present T...
The paper introduces Culture-MMT, a benchmark for evaluating how well machine translation systems can reconstruct meaning from distributed evidence in social‑media posts. It contains 498 curated, de‑identified user‑generated content instances across 20 domains, classified into Symbol, Visual, and Hybrid based on the type of evidence required. Experiments with 17 model configurations show that real images significantly improve translation for Visual and Hybrid cases, while random images do not, underscoring the importance of accurate visual grounding and cultural adaptation.
By Dingming Li, Yiwen Qiu, Linjuan Wu, Hongxing Li, Zixuan Wang, Xinze Lyu, Ye Guo, Daoxin Zhang, Zhe Xu, Weiming Lu, Yongliang Shen
arXiv:2609.18156v1 Announce Type: new
Abstract: Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating l...
By Jianan Wu
Large Language Models (LLMs) are being tested as a cost‑effective alternative to human judgment for evaluating machine translation (MT). This study compares LLM agreement with human annotators on two MT quality schemes—Multidimensional Quality Metrics (MQM) and Error Span Annotation (ESA)—across 70 language pairs and public WMT23/25 data. Results show that while LLMs sometimes match or exceed human agreement, performance varies widely by scheme, language, and domain, and remains unreliable in many settings. The work also identifies specific challenges for both humans (e.g., fine‑grained MQM, low‑resource languages) and LLMs (e.g., minor errors, wrong language variants, span annotation).
By Hala Almaghout, Christian Federmann, Qin Gao
The paper investigates how machine translation can be tailored to specific audiences and intents, a capability enabled by large language models (LLMs). By systematically evaluating purpose-driven MT across 50 languages, 5 model sizes, and 8 text domains, the authors find that explicit instructions significantly improve translation adaptiveness, especially for informal domains, larger models, and higher-resource languages. They also show that traditional MT metrics often penalize adapted translations and that models can self-generate useful instructions from context, closing a large portion of the adaptiveness gap.
By Raphael Merx, Ekaterina Vylomova, Trevor Cohn
Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cultural backgrounds. Therefore, adapting memes acr...
arXiv:2609.00491v1 Announce Type: new
Abstract: Communicating across cultures is inherently challenging, especially through culturally dense and ambiguous formats like memes. While people expect larg...
By Hangxiao Zhu, Suliu Qin, Zhuoyan Li, Ming Jiang, Yu Zhang, Meng Xia