# Towards End-to-End Multilingual Metaphor Processing: Integrating Detection, Translation, and Evaluation

arXiv: https://arxiv.org/abs/2608.04260 · HTML: https://mlsanity.com/paper/2608.04260/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation](https://mlsanity.com/paper/2112.13833/) (2021)
- [MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language](https://mlsanity.com/paper/2406.13698/) (2024)
- [Metaphor and Large Language Models: When Surface Features Matter More than Deep Understanding](https://mlsanity.com/paper/2507.15357/) (2025)
- [Towards a resource for multilingual lexicons: an MT assisted and human-in-the-loop multilingual parallel corpus with multi-word expression annotation](https://mlsanity.com/paper/2011.03783/) (2020)
- [Dutch Metaphor Extraction from Cancer Patients' Interviews and Forum Data using LLMs and Human in the Loop](https://mlsanity.com/paper/2511.06427/) (2025)
- [Non-Linear Scoring Model for Translation Quality Evaluation](https://mlsanity.com/paper/2511.13467/) (2025)
- [MetaHOPE: A Metaphor-Oriented Evaluation Framework for Analysing MT and LLM Translation Errors](https://mlsanity.com/paper/2607.00848/) (2026)
- [Measuring the practice of shared-decision making (OPTION12): An Investigation into Open-sourced Smaller LLMs (OS-sLLMs) for Better Privacy and Sustainability](https://mlsanity.com/paper/2607.06127/) (2026)

## Abstract (arXiv)

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.