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<p>Neural Machine Translation (NMT) for low-resource languages such as Assamese faces significant challenges due to the scarcity of parallel corpora. To address this, we propose a novel zero-shot translation framework that leverages linguistic proximity between Assamese and Bengali, enabling English-to-Assamese translation without direct parallel data. Our approach combines a multilingual transfer learning architecture based on mBART with cross-lingual embedding alignment via Procrustes optimization, unifying English, Bengali, and Assamese into a shared semantic space. We further mitigate out-of-vocabulary (OOV) challenges through subword tokenization, embedding projection, and dynamic vocabulary expansion, while advanced language tagging mechanisms reduce off-target errors by enforcing explicit language distinctions during inference. We evaluate our framework on a curated English–Assamese dataset, achieving a BLEU score of 28.65–7.53 points higher than direct translation baselines. Human evaluations highlight significant improvements, with adequacy and fluency scores of 4.5/5 and 4.6/5, respectively, and an 8.4% error rate reduction. Ablation studies confirm the critical roles of embedding alignment and language-aware tagging, while inference speed analysis demonstrates a 26.9% improvement over baseline models. This work demonstrates that (pivot based) transfer learning and cross-lingual alignment is effective in bridging resource gaps for linguistically proximate languages.</p>

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Zero-shot English–Assamese neural machine translation via pivot-based cross-lingual embedding alignment and transfer learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Basab Nath,
  • Yonis Gulzar

摘要

Neural Machine Translation (NMT) for low-resource languages such as Assamese faces significant challenges due to the scarcity of parallel corpora. To address this, we propose a novel zero-shot translation framework that leverages linguistic proximity between Assamese and Bengali, enabling English-to-Assamese translation without direct parallel data. Our approach combines a multilingual transfer learning architecture based on mBART with cross-lingual embedding alignment via Procrustes optimization, unifying English, Bengali, and Assamese into a shared semantic space. We further mitigate out-of-vocabulary (OOV) challenges through subword tokenization, embedding projection, and dynamic vocabulary expansion, while advanced language tagging mechanisms reduce off-target errors by enforcing explicit language distinctions during inference. We evaluate our framework on a curated English–Assamese dataset, achieving a BLEU score of 28.65–7.53 points higher than direct translation baselines. Human evaluations highlight significant improvements, with adequacy and fluency scores of 4.5/5 and 4.6/5, respectively, and an 8.4% error rate reduction. Ablation studies confirm the critical roles of embedding alignment and language-aware tagging, while inference speed analysis demonstrates a 26.9% improvement over baseline models. This work demonstrates that (pivot based) transfer learning and cross-lingual alignment is effective in bridging resource gaps for linguistically proximate languages.