The research offers a Lotus Effect-Attention-based Bi-directional Gated Recurrent Unit (LE-Att-Bi-GRU) deep learning model for automatic translation quality assessment that improves semantic representation by incorporating a lotus-inspired division method that decreases noise and focuses essential semantic cues.
Abstract
Translation quality assessment functions as a vital component which Master of Translation and Interpreting programs use to evaluate students’ language skills and their translation abilities. Conventional human-based translation scoring is highly subjective, labour-intensive, and lacking in reliability. To overcome these limitations, the research offers a Lotus Effect-Attention-based Bi-directional Gated Recurrent Unit (LE-Att-Bi-GRU) deep learning (DL) model for automatic translation quality assessment. The Translation Quality Evaluation dataset incorporates large parallel corpora involving of MTI student translations, expert reference translations, and consistent bilingual datasets. Collected text undergoes pre-processing steps such as text normalization and sentence alignment, which are used to ensure data standardisation and accuracy. Feature extraction adopts Term Frequency-Inverse Document Frequency (TF-IDF) to quantify linguistic features such as terminology accuracy, lexical richness, and structural coherence, and Sentence-Bidirectional Encoder Representations from Transformers (Sentence-BERT) embeddings to capture semantic equivalence between source and translated texts. The proposed LE-Att-Bi-GRU model improves semantic representation by incorporating a lotus-inspired division method that decreases noise and focuses essential semantic cues. The Bi-GRU captures bidirectional contextual dependencies, while the integrated attention module dynamically assigns weight to essential translation segments, developing translation accuracy, and style evaluation. The supervised model is trained using expert-annotated quality scores as ground truth. Experimental outcomes reveal high correlation with professional assessment standards and superior performance compared to precision (95.1%), accuracy (92.3%), F-measure (94.2%), AUC (0.926), recall (93.4%), Pearson (0.842), and Spearman (0.829). This research contributes to automatic, scalable, and pedagogically meaningful evaluation for MTI training, curriculum development, and future AI-assisted translation evaluation systems. Graphical abstract show the Overview of the proposed LE-Att-Bi-GRU framework for automatic MTI translation quality evaluation using semantic and linguistic features. Graphical abstract show the Overview of the proposed LE-Att-Bi-GRU framework for automatic MTI translation quality evaluation using semantic and linguistic features.
Ensuring high-quality output in Neural Machine Translation (NMT) systems remains a central challenge, especially in applications with critical fluency, grammatical accuracy, and semantic fidelity. While significant advancements have been made in model architecture, less attention has been given to the role of data preprocessing and post-translation evaluation, both of which are essential for enhancing translation reliability and scalability. This study introduces a dual-stage framework that integrates linguistic preprocessing techniques and a Convolutional Neural Network (CNN)-based classifier for translation quality assessment. We use the English--Spanish Translation Dataset by Lonnie Qin to train a lightweight Seq2Seq NMT model with and without preprocessing steps, including tokenization, lowercasing, lemmatization, and normalization. Translation quality is evaluated using BLEU, METEOR, and ROUGE metrics. A 1D CNN is trained as a lightweight post-translation screening model using BLEU-derived weak labels for large-scale binary supervision and a smaller human-labeled subset for finer-grained validation. The purpose of this classifier is not merely to reproduce a fixed BLEU cutoff, but to learn reusable quality patterns that can support rapid quality flagging in settings where repeated reference-based evaluation or manual review is impractical. It is important to note that the CNN is not used as the translation generator itself, but as a lightweight post-translation quality estimation module, selected to examine whether reliable sentence-level quality screening can be achieved in resource-conscious and real-time settings without resorting to heavier transformer-based classifiers. Experimental results demonstrate that preprocessing consistently improves the translation accuracy of this lightweight model, with BLEU scores increasing by 8.4 points and METEOR by 4 points; these gains are specific to the simple Seq2Seq model studied here and are not claimed to transfer to stronger architectures. The CNN classifier achieves 88.7 accuracy in binary classification and a macro F1-score of 0.82 in multi-class evaluation. The integrated pipeline improves both the generation and validation of translations, making it suitable for real-time quality assurance and educational use cases. The proposed approach highlights the often-underestimated impact of preprocessing and the efficiency of CNNs in evaluating translation quality. Together, they form a robust, scalable, and adaptable solution for improving translation outputs. This framework offers substantial potential for deployment in educational and professional language-support settings, while domain-specific applications such as legal or medical translation require further validation on specialized corpora.
Zahra Moradi· Journal for Language Technol...· 0 citations
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.
Results indicate that combining attention-based Transformer translation with practical deployment features — language detection, explainability, history management, and reporting — can deliver an accurate, scalable, and user-accessible multilingual translation platform.
Chinmaya Md, Supreetha Hd· International journal of tec...· 0 citations
Neural machine translation (NMT) has witnessed substantial advancements, leveraging its learning capability to deliver highly accurate translations. Nevertheless, the efficacy of NMT models is contingent upon the accessibility of extensive-scale, high-quality training data, and its performance suffers notably in the absence of such datasets. To tackle this challenge, we propose a semantic distance augmentation (SDA) method that integrates syntactic information from constituency parse trees into the NMT encoder to optimize self-attention. Specifically, the source language sentences in the training set are analyzed by constituency parse analysis and the semantic distance attention matrix is constructed. Then, a fusion strategy is designed to integrate this matrix into the self-attention weight, enhancing the representation of the source sentences. In addition, a SDA length-aware strategy is proposed to adaptively control the contribution of semantic distance in the attention computation. Empirical evaluations across multiple low-resource language pairs reveal that the SDA method achieves statistically significant improvements in translation quality over the strong baseline, without requiring additional training data or increasing model complexity.
Fuxue Li, Hong Yan, Chuncheng Chi et al.· PeerJ Computer Science· 0 citations
Intelligent semantic information processing and adaptive knowledge generation have become key enabling technologies for next-generation communication and information systems. This study proposes a New Quality of Translation Productivity framework based on Large Language Models (NQTP-LLM) for intelligent multilingual information processing and adaptive educational support. The framework integrates transformer-based neural machine translation, Direct Preference Optimization (DPO), Retrieval-Augmented Generation (RAG), semantic embedding representation, and human-in-the-loop optimization to enhance contextual consistency, semantic fidelity, and translation efficiency. A multimodal translation evaluation architecture is established using semantic feature extraction, contextual knowledge retrieval, quality assessment, and adaptive feedback mechanisms. Experiments conducted on the AI vs TTM Translation Evaluation Dataset demonstrate that the proposed framework achieves a BLEU score of 0.961, with substantial improvements in METEOR, ROUGE, chrF, BERTScore, COMET, BLEURT, Google-BLEU, and NIST metrics while reducing post-editing effort by 3.9%. The results verify the effectiveness of integrating intelligent knowledge retrieval, semantic information fusion, and adaptive optimization for high-accuracy multilingual information processing. The proposed framework provides a practical approach for intelligent communication systems, semantic information services, human–AI collaborative decision support, and next-generation knowledge-centric digital environments.
W. Zhou, X. Zhou· Advanced Electromagnetics· 0 citations
The results indicate that a moderately sized, shared self-attention architecture can deliver production-quality multilin-gual translation within the resource constraints of an academic de-ployment, while surfacing clear directions – low-resource language coverage, domain adaptation, and speech-based extension – for con-tinued development.
Darshan Gowda D H and Dr. Kruti R· International Journal of Adv...· 0 citations