Aug 2026· International Journal of Information Technologies and Systems Approach· 0 citations
Abstract
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 module is introduced to dynamically fuse n-gram-level surface features on top of the Transformer high-level representation, preserving fine-grained error signals that are easily smoothed by attention mechanisms. Finally, a label-aware focus loss function is designed to alleviate the extreme imbalance between positive and negative samples. This function supports joint prediction of four types of fine-grained error labels. Results showed that on the large-scale benchmark of WMT QE Shared Task 2023, covering six language pairs, alignment-aware multi-granularity tagging achieved 78.6% in Micro-F1, 72.3% in Macro-F1, and improved Recall@Rare to 62.3%, especially excelling in low-frequency error types and significantly outperforming the baseline model.
DI performs Direct Preference Optimization (DPO) after supervised fine-tuning to strengthen task alignment with human preferences, and introduces a controlled decoding process that enforces fixed output formats and restricts predictions to candidate sets.
Improved robustness and explainability for automatic Chinese learner error correction is demonstrated by an alignment consistency loss to ensure character-level consistency, and the combination of word-segmentation augmentation and multi-reference soft-label training to reduce conflicts caused by segmentation differenc...
Y.-L. Diao, W. Gao· Advanced Electromagnetics· 0 citations
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...
Fuxue Li, Hong Yan, Chuncheng Chi et al.· PeerJ Computer Science· 0 citations
The results show that hybrid ensemble methods can improve token-level accuracy in low-resource POS tagging, while also revealing a trade-off between frequent-tag accuracy and rare-tag robustness.
Abdelouahed Moussaoui, Nor-Eddine Azalmad, Said Bahassine et al.· Information· 0 citations
A robust preprocessing pipeline incorporating Google Translation, spaCy-based NER with hyphenated-word normalization, and a future-event-aware fallback logic is proposed, achieving a 40 percent reduction in inference latency and closing the linguistic generalization gap left by previous works.
Rishabh Kumar, Aditya Kumar· Journal of Frontiers in Mult...· 0 citations
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.
