ArticleScientific reports2025
Deep neural architectures for Kashmiri-English machine translation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Zero-shot English-Assamese neural machine translation via pivot-based cross-lingual embedding alignment and transfer learning.Scientific reports · 2026Article
- Cross-lingual sparse-MoE distillation for efficient low-resource assamese-english and bodo-english translation.Scientific reports · 2026Article
- Context-aware KazRoBERTa with transformer-based candidate re-ranking for Kazakh spelling correction.Frontiers in artificial intelligence · 2026Article
- Navigating AI feedback in translation training: how text type, proficiency, and attitude shape students' acceptance behaviors.Frontiers in artificial intelligence · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
This paper presents the first comprehensive deep learning-based Neural Machine Translation (NMT) framework for the Kashmiri-English language pair. We introduce a high-quality parallel corpus of 270,000 sentence pairs and evaluate three NMT architectures: a basic encoder-decoder model, an attention-enhanced model, and a Transformer-based model. All models are trained from scratch using byte-pair encoded vocabularies and evaluated using BLEU, GLEU, ROUGE, and ChrF + + metrics. The Transformer architecture outperforms RNN-based baselines, achieving a BLEU-4 score of 0.2965 and demonstrating superior handling of long-range dependencies and Kashmiri's morphological complexity. We further provide a structured linguistic error analysis and validate the significance of performance differences through bootstrap resampling. This work establishes the first NMT benchmark for Kashmiri-English translation and contributes a reusable dataset, baseline models, and evaluation methodology for future research in low-resource neural translation.
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Registered trials
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