Automatic Assessment Model for Chinese-English Scientific Translation Quality Based on Contrastive Learning
TL;DR
A Contrastive Learning-based Chinese-English Scientific Translation Quality Evaluation model (C-TQE), which provides an effective solution for large-scale scientific translation quality assessment and facilitates the accurate international communication of multidisciplinary engineering research, including electromagnetic and antenna-related studies.
Abstract
Assessing the quality of scientific literature translation remains challenging because of strong subjectivity, dense domain-specific terminology, and the limited availability of standardized reference translations. These issues are particularly relevant for the international dissemination of research in advanced electromagnetic engineering, where precise multilingual communication supports the reliable exchange of knowledge on electromagnetic waves, antennas, and propagation technologies. This paper proposes a Contrastive Learning-based Chinese-English Scientific Translation Quality Evaluation model (C-TQE). By constructing multi-level positive and negative sample pairs, the model learns the relative ordinal relationships of translation quality within a shared representation space. A dual-encoder architecture encodes source sentences and candidate translations through a shared pre-trained language model, while a contrastive loss function draws high-quality translations closer to the source representation and separates low-quality ones. To address the characteristics of scientific texts, a term-aware negative sampling strategy exploits domain dictionaries and syntactic structures to generate semantically similar but terminologically incorrect examples. Experiments on 11, 238 human-annotated instances from the WMT20–22 Chinese-English scientific translation tasks show that C-TQE achieves a Kendall’s tau correlation coefficient of 0.564 with human judgments, outperforming COMET (0.512) and BLEURT (0.497). Ablation studies confirm the effectiveness of term-aware negative sampling and the contrastive learning objective, while diagnostic analysis demonstrates high consistency in evaluating terminological accuracy and syntactic structures. The proposed framework provides an effective solution for large-scale scientific translation quality assessment and facilitates the accurate international communication of multidisciplinary engineering research, including electromagnetic and antenna-related studies.