TL;DR
A zero-shot translation enhancement algorithm based on dual semantic decoupling and explicit path regularization that improves the robustness and controllability of zero-shot translation in low-resource and cross-linguistic scenarios.
A semantic distance augmentation (SDA) method that integrates syntactic information from constituency parse trees into the NMT encoder to optimize self-attention and achieves statistically significant improvements in translation quality over the strong baseline, without requiring additional training data or increasing...
An Efficient Dual-BERT Adversarial Network (DBAN) is proposed to improve the translation of noisy UGC by integrating contextual representation learning with adversarial training and significantly improves contextual understanding and cross-lingual semantic alignment while maintaining computational efficiency.
This paper proposes an alignment-aware multi-granularity tagging framework. First, this method uses a cross-lingual pre-trained model to encode source and target language contexts jointly while explicitly modeling-level bilingual correspondences via a learnable soft alignment layer. Second, a gated local enhancement mo...
Recent advances in large language models (LLMs) have led sign language translation (SLT), the task of converting sign-language videos into spoken-language text, to increasingly adopt LLMs as textual backbones. However, despite their strong language modeling capabilities, existing LLM-based SLT methods often undermine r...
The results show that multilingual transfer is the dominant factor in extremely low-resource Bantu translation while eliminating the need for heuristic proxy selection, and all systems fail to preserve tonal diacritics, highlighting an open challenge.
The results of this research demonstrate that multimodal fusion and cross-language alignment mechanisms offer effective approaches to improve the accuracy and reliability of multimodal translation systems.
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