Article: A neural network-based quality assessment model for English-to-Chinese text translation Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.56 pp.32 - 51 Abstract: Addressing the urgent need for cross-language text translation quality assessment, this paper proposes a neural network-based model for evaluating English-Chinese translation quality. Current widely adopted automated evaluation methods exhibit significant limitations in handling specialised terminology and nuanced semantics, particularly when addressing culture-specific concepts. The neural network model constructed in this study integrates deep semantic representation with contextual correlation analysis, achieving remarkable results on the Chinese-English test set of the public WMT 2020 metrics shared task dataset. It achieved a core correlation metric (Pearson's r) of 0.682, along with a multi-dimensional classification evaluation (macro-F1) of 0.689 and a ranking quality metric (normalised discounted cumulative gain @10) of 0.927, comprehensively outperforming mainstream baseline models. This model provides a reliable technical tool for cross-language text quality control. Inderscience Publishers - linking academia, business and industry through research

Title: A neural network-based quality assessment model for English-to-Chinese text translation

Authors: Tiejiang Hu

Addresses: Department of Public Courses Teaching, Hunan Communication Polytechnic, Changsha, 410132, China

Abstract: Addressing the urgent need for cross-language text translation quality assessment, this paper proposes a neural network-based model for evaluating English-Chinese translation quality. Current widely adopted automated evaluation methods exhibit significant limitations in handling specialised terminology and nuanced semantics, particularly when addressing culture-specific concepts. The neural network model constructed in this study integrates deep semantic representation with contextual correlation analysis, achieving remarkable results on the Chinese-English test set of the public WMT 2020 metrics shared task dataset. It achieved a core correlation metric (Pearson's r) of 0.682, along with a multi-dimensional classification evaluation (macro-F1) of 0.689 and a ranking quality metric (normalised discounted cumulative gain @10) of 0.927, comprehensively outperforming mainstream baseline models. This model provides a reliable technical tool for cross-language text quality control.

Keywords: neural network; translation quality assessment; cross-language application.

DOI: 10.1504/IJICT.2026.153910

International Journal of Information and Communication Technology, 2026 Vol.27 No.56, pp.32 - 51

Received: 07 Jan 2026
Accepted: 03 Feb 2026
Published online: 29 May 2026 *

Title: A neural network-based quality assessment model for English-to-Chinese text translation

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