发表机构
Centre for Linguistics, Humanities, Leiden University; LIACS, Leiden University; BDS, Leiden University Medical Centre（莱顿大学人文学院语言学中心; 莱顿大学LIACS; 莱顿大学医学中心BDS）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出MetaHOPE框架，基于错误严重性标注隐喻翻译错误，通过人工标注语料评估GoogleMT、GPT5.4和Hunyuan-7b在英汉互译中的表现，并构建双语平行资源。
AI 中文摘要
在这篇观点论文中，我们提出了MetaHOPE，一个错误严重性感知的标注框架，用于评估隐喻翻译。隐喻对机器翻译（MT）和自然语言理解与处理（NLU、NLP）构成挑战，因为它具有语义复杂性、上下文依赖性和文化嵌入性等特征，可能导致NLP模型出现歧义问题。为了研究最先进的NLP模型在翻译隐喻方面的表现，我们选择了三个代表性系统，即GoogleMT、GPT5.4和Hunyuan-7b，作为神经机器翻译（NMT）模型和大语言模型（LLM）。我们使用了两个人工标注的隐喻语料库，包括VUAMC和PSUCMC，用于英译中和中译英任务。我们使用的原始语料是单语的，我们使用MetaHOPE框架进行了错误标注，并生成了人工后编辑的金标准参考，作为双语使用的新资源。我们相信，用于隐喻翻译标注的MetaHOPE评估框架、平行语料资源以及对最先进自动翻译模型的错误分析，将对隐喻翻译研究领域有所裨益。论文被接收后，我们将公开共享我们的资源。
英文摘要
In this opinion paper, we propose MetaHOPE, an error severity-aware annotation framework for evaluating metaphor translations. Metaphors present challenges for machine translation (MT) and natural language understanding and processing (NLU, NLP), because it presents the features of semantic complexity, contextual dependency, and cultural embeddings that can lead to ambiguity issues for NLP models. To investigate how state-of-the-art NLP models perform on translating metaphors, we select three representative systems, i.e., GoogleMT, GPT5.4, and Hunyuan-7b as Neural MT (NMT) models and LLMs. We used two human-annotated metaphor corpora, including VUAMC and PSUCMC for English-to-Chinese and Chinese-to-English translation purposes. The original corpora we used are monolingual, where we carried out error annotation using the MetaHOPE framework, and also produced the human post-edited gold reference for bilingual use as a new resource. We believe the MetaHOPE evaluation framework for metaphor translation annotation, the parallel corpora resources, and the error analysis on SOTA automatic translation models can be useful and shed some light for the field of metaphor translation study. We share our resources publicly at github.com/Jiahui84/MetaHOPE
CommentsTo appear in the Proceedings of the 9th International Conference on Natural Language and Speech Processing (ICNLSP 2026), Trento, Italy, September 2026
