构建端到端多语言隐喻处理框架，融合检测、翻译与评估
Towards End-to-End Multilingual Metaphor Processing: Integrating Detection, Translation, and Evaluation
- 结合语言学理论与大模型，统一建模跨语言隐喻检测
- 设计面向隐喻的翻译评估方法，支持人工与自动评价
- 适合多语言自然语言处理研究者，尤其关注修辞理解者
隐喻语言仍是多语言自然语言处理的重大挑战，因其成功解读与翻译需超越字面语义的推理。现有研究多将隐喻检测、机器翻译与翻译评估作为独立任务处理，缺乏对三者整合的系统探索。本博士课题旨在构建一个端到端的多语言隐喻处理框架，包含三个互补方向：(1) 跨语言鲁棒隐喻检测；(2) 面向人类评估与自动质量估计的隐喻导向翻译评估；(3) 隐喻检测与翻译评估的联合建模。研究将融合语言学理论与大语言模型最新进展，开发新数据集、标注方法、评估基准与自动评估方法，实现隐喻感知的机器翻译系统。预期成果为一个统一框架，提升多语言NLP系统在处理比喻语言时的开发与评估能力。
原文摘要 · Abstract (English)
Metaphorical language remains a major challenge for multilingual natural language processing because successful interpretation and translation require reasoning beyond literal lexical meaning. Existing research has largely investigated metaphor detection, machine translation, and translation evaluation as separate tasks, while little work has explored how these components can be integrated into a unified computational framework. This PhD proposal aims to develop an end-to-end framework for multilingual metaphor processing consisting of three complementary research directions: (1) robust metaphor detection across languages, (2) metaphor-oriented translation evaluation for both human assessment and automatic quality estimation, and (3) joint modelling that connects metaphor detection with translation evaluation. The proposed research will combine linguistic theory with recent advances in large language models to develop new datasets, annotation methodologies, evaluation benchmarks, and automatic evaluation approaches for metaphor-aware machine translation. The expected outcome is a unified framework that improves both the development and evaluation of multilingual NLP systems when processing figurative language.
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