Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation

• URL: http://arxiv.org/abs/2601.07338v1
• Date: Mon, 12 Jan 2026 09:03:42 GMT
• Title: Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation
• Authors: Yanzhi Tian, Cunxiang Wang, Zeming Liu, Heyan Huang, Wenbo Yu, Dawei Song, Jie Tang, Yuhang Guo,
• Abstract summary: We propose a novel agentic translation evaluation framework, centered by a reflective Core Agent that invokes specialized sub-agents.<n> Experimental results indicate the efficacy of RATE, achieving an improvement of at least 3.2 meta score compared with current metrics.
• Score: 57.11989521509119
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have significantly advanced Machine Translation (MT), applying them to linguistically complex domains-such as Social Network Services, literature etc. In these scenarios, translations often require handling non-literal expressions, leading to the inaccuracy of MT metrics. To systematically investigate the reliability of MT metrics, we first curate a meta-evaluation dataset focused on non-literal translations, namely MENT. MENT encompasses four non-literal translation domains and features source sentences paired with translations from diverse MT systems, with 7,530 human-annotated scores on translation quality. Experimental results reveal the inaccuracies of traditional MT metrics and the limitations of LLM-as-a-Judge, particularly the knowledge cutoff and score inconsistency problem. To mitigate these limitations, we propose RATE, a novel agentic translation evaluation framework, centered by a reflective Core Agent that dynamically invokes specialized sub-agents. Experimental results indicate the efficacy of RATE, achieving an improvement of at least 3.2 meta score compared with current metrics. Further experiments demonstrate the robustness of RATE to general-domain MT evaluation. Code and dataset are available at: https://github.com/BITHLP/RATE.

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