arXiv Computation and Language By Jiahui Liang, Lifeng Han

MetaHOPE: A Metaphor-Oriented Evaluation Framework for Analysing MT and LLM Translation Errors

Read the original on arXiv Computation and Language →

MetaHOPE is an error‑severity‑aware annotation framework designed to evaluate how well machine translation (MT) and large language models (LLMs) translate metaphors. The authors applied MetaHOPE to three state‑of‑the‑art systems—GoogleMT, GPT5.4, and Hunyuan‑7b—using two human‑annotated metaphor corpora (VUAMC and PSUCMC) for English‑to‑Chinese and Chinese‑to‑English translation. They also produced a bilingual post‑edited gold reference, creating a new resource for metaphor translation research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv Computation and Language
Oct 9

Large Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged

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
arXiv Computation and Language
Oct 16

M$^3$R-Bench: A Unified Benchmark for Evidence-Grounded Multimodal Metaphor Understanding

M$^3$R-Bench is a new benchmark comprising 1,000 human‑annotated image‑text pairs that jointly label metaphor occurrence, Target‑Source mapping, and sentiment, and provide step‑by‑step explanations for evidence identification, mapping, and sentiment inference. Existing models often rely on textual shortcuts and produce mappings that conflict with visual evidence, as shown by evaluations on this dataset. To address this, the authors propose M$^3$R‑Reasoner, a model that uses curriculum‑based reasoning supervision and task‑aware reinforcement learning, achieving a 37.90% Joint Label Accuracy and outperforming both proprietary baselines and GPT‑5.5 on evidence and sentiment justification.

By Hong Jiang, Junnan Zhu, Jingwang Huang, Xiao Sun, Yuming Yang, Jiang Zhong, Ruirui Chen, Jingman Shi, Hao Wu, Nayu Liu, Xinyi Jiang, Kaiwen Wei
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