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This research proposes an end-to-end framework for multilingual metaphor processing that integrates metaphor detection, translation, and evaluation into a cohesive system. By leveraging linguistic theory and advancements in large language models, the study aims to enhance the robustness of metaphor detection across languages and improve translation evaluation methods. The key result is the development of new datasets and benchmarks that facilitate better metaphor-aware machine translation, ultimately advancing multilingual natural language processing capabilities.
A unified framework for multilingual metaphor processing could revolutionize how figurative language is understood and translated across languages.
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.
