Cross-lingual Machine Translation (MT) quality estimation plays a crucial
role in evaluating translation performance. GEMBA, the first MT quality
assessment metric based on Large Language Models (LLMs), employs one-step
prompting to achieve state-of-the-art (SOTA) in system-level MT quality
estimation; however, it lacks segment-level analysis. In contrast,
Chain-of-Thought (CoT) prompting outperforms one-step prompting by offering
improved reasoning and explainability. In this paper, we introduce
Knowledge-Prompted Estimator (KPE), a CoT prompting method that combines three
one-step prompting techniques, including perplexity, token-level similarity,
and sentence-level similarity. This method attains enhanced performance for
segment-level estimation compared with previous deep learning models and
one-step prompting approaches. Furthermore, supplementary experiments on
word-level visualized alignment demonstrate that our KPE method significantly
improves token alignment compared with earlier models and provides better
interpretability for MT quality estimation. Code will be released upon
publication.

本文提出一种基于 CoT 提示的 KPE 方法，该方法结合了 Perplexity、Token-Level 相似性和 Sentence-Level 相似性的技术。实验证明，与以前的深度学习模型和单步提示方法相比，该方法的分段估计性能得到了显著提高，并且提供了更好的 MT 质量估计可解释性。