Understand
This paper introduces the Comprehensive AI-assisted Translation Edit Ratio (CATER), a novel and fully prompt-driven framework for evaluating machine translation (MT) quality.
- Leveraging large language models (LLMs) via a carefully designed prompt-based protocol, CATER expands beyond traditional reference-bound metrics, offering a multidimensional, reference-independent evaluation that addresses linguistic accuracy, semantic fidelity, contextual coherence, stylistic appropriateness, and information completeness.
- CATER's unique advantage lies in its immediate implementability: by providing the source and target texts along with a standardized prompt, an LLM can rapidly identify errors, quantify edit effort, and produce category-level and overall scores.
- This approach eliminates the need for pre-computed references or domain-specific resources, enabling instant adaptation to diverse languages, genres, and user priorities through adjustable weights and prompt modifications.