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.
Sequence labeling is a fine-grained information extraction task, yet existing large language model-based approaches suffer from insufficient domain alignment and low inference efficiency. To address these issues, we propose DIRECT, a framework that addresses these issues through training-time optimization and inference-time rectification. Specifically, DIRECT 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. To further improve efficiency, a template-filling mechanism requires the model to generate only label tokens while reusing prefixed content through the KV Cache, thus reducing redundant computation. Experimental results on eight datasets demonstrate that DIRECT achieves significant improvements in both performance and efficiency compared to existing methods.
Yilei Wang, Jiaxin Gan, Kexuan Zhang et al.· 0 citations
Current automatic error correction methods for Chinese learners often focus on superficial word- or sentence-level processing and are affected by inconsistent annotation standards, resulting in limited generalization to real learner texts and causing misalignment or overcorrection. To improve grammatical correctness, semantic fidelity, and instructional relevance, this paper constructs a multi-granularity lexical representation method. Using characters as the basic unit, the method integrates three types of information: characters, words, and pinyin. These representations are learned through projection processing, concatenated into a unified embedding, and fed into a pre-trained Chinese BERT encoder. A dual-task head consisting of sequence labeling and lightweight generation is deployed on the shared BERT encoder for collaborative optimization. Key innovations include 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 differences. Experimental results show that the method achieves granular alignment consistency above 0.800, corrected-original sentence similarity of 0.888, and an overcorrection rate as low as 0.041. These results demonstrate improved robustness and explainability for automatic Chinese learner error correction.
Y. 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 model complexity.
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.
