An Explainable Approach to Document-level Translation Evaluation with Topic Modeling
The advent of NMT has expanded the scope of translation beyond isolated sentences, enabling context to be preserved across paragraphs and documents. However, current evaluation metrics largely remain restricted to the sentence level and typically depend on reference translations. Without references, existing metrics cannot provide a clear basis for their quality assessments. To address these limitations, we propose an evaluation framework that independently extracts and compares latent topic structures within source and translated texts. This framework utilises various topic modelling techniques, including LSA, LDA and BERTopic, to achieve this. Our methodology captures statistical frequency information and semantic context, providing a comprehensive evaluation of the entire document. It aligns key topic tokens across languages using a bilingual dictionary and quantifies thematic consistency via cosine similarity. This allows us to evaluate how faithfully the translation maintains the thematic integrity of the source text, even in the absence of reference translations. To this end, we used a large scale dataset of 9.38 million Korean to English sentence pairs from AI Hub, which includes pre evaluated BLEU scores. We also calculated CometKiwi, a state of the art, reference free metric for this dataset, in order to conduct a comparative analysis with our proposed, topic based framework. Through this analysis, we confirmed that, unlike existing metrics, our framework evaluates the differentiated attribute of document level thematic units. Furthermore, visualising the key tokens that underpin the quantitative evaluation score provides clear insight into translation quality. Consequently, this study contributes to effectively complementing the existing translation evaluation system by proposing a new metric that intuitively identifies whether the document's theme has been preserved.