  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.
