The scarcity of parallel corpora for Mongolian and Chinese constrains the performance of Mongolian-Chinese neural machine translation (NMT), particularly manifesting in inadequate accuracy in translating specialized terminology. To address this limitation, this study adopts a lexically constrained augmentation strategy that constructs pseudo-source sentences by appending Chinese constraint words to Mongolian source texts, while enforcing the inclusion of these constraints in the output to improve translation accuracy. However, this approach presents two inherent drawbacks: processing pseudo-sentences with a single encoder tends to induce semantic interference, while the introduced constraint words may exacerbate alignment errors during decoding. To overcome these limitations, this paper propose a Constraint-Augmented Mongolian-Chinese NMT method (CANMT) based on dynamic feedback alignment. The method employs a dual-encoder architecture to isolate bilingual representations, coupled with a dynamic feedback alignment module that progressively reduces alignment errors through iterative reffnement, thereby enhancing overall translation performance.

Logit Knowledge Distillation has gained substantial research interest in recent years due to its simplicity and lack of requirement for intermediate feature alignment; however, it suffers from limited interpretability in its decision-making process. To address this, we propose implicit Clustering Distillation (iCD): a simple and effective method that mines and transfers interpretable structural knowledge from logits, without requiring ground-truth labels or feature-space alignment. iCD leverages Gram matrices over decoupled local logit representations to enable student models to learn latent semantic structural patterns. Extensive experiments on benchmark datasets demonstrate the effectiveness of iCD across diverse teacher-student architectures, with particularly strong performance in fine-grained classification tasks---achieving a peak improvement of +5.08% over the baseline.

Digitizing historical Mongolian newspapers is challenging due to severe degradation and unique script properties. Elongated words and artifacts, like ink bleeding cause geometric distortion. These issues limit traditional OCR performance. To address these issues, we propose MGL-LMM, a Generative OCR Paradigm specifically designed for degraded agglutinative scripts. Our framework integrates a topology-preserving visual normalization strategy. This maintains structural integrity under anisotropic distortion. To stabilize training, we employ a decoupled curriculum learning scheme that transitions from word-level glyph morphologies to line-level contextualization. By projecting high-fidelity visual features directly into the semantic space of a 1.5B-scale Large Language Model, MGL-LMM performs end-to-end generative reasoning. This allows the model to leverage contextual language priors to effectively reduce visual ambiguities and provide semantic restoration for obscured characters. Extensive experiments demonstrate that our method achieves state-of-the-art performance, reducing the Character Error Rate (CER) from 27% to 11%. This significant improvement demonstrates that generative decoding with language priors is fundamentally more effective than purely visual recognition under severe degradation.

Anomaly detection plays a crucial role in quality control within the manufacturing industry. Traditional computer vision approaches typically adopt reconstruction-based strategies, where the distribution of normal features is modeled to suppress the reconstruction of anomalous regions. Although these methods generally require fewer data, they often struggle to simultaneously improve reconstruction quality and detection performance. Experimental results indicate that high-level semantic context features exhibit strong capabilities in identifying anomalies, while low-level features contribute to the representation of global characteristics. Based on this, the paper uses discrete wavelet transform (DWT) to decompose images into low- and high-frequency components, extracting both global and local features. The input image is encoded in parallel using convolutional encoders and fused with the base features extracted through DWT. To further enhance detection performance, we propose a customized feature fusion module inspired by Feature Pyramid Networks (FPN), which integrates spatial and channel attention mechanisms to strengthen the model's ability to capture inter-feature relationships. Finally, extensive comparative and ablation experiments conducted on public benchmark datasets demonstrate the effectiveness of the proposed method.

The analysis of Mongolian dependency syntax has always been an important part of Mongolian semantic analysis, machine translation, semantic role annotation and other tasks. However, the corpus of Mongolian as a low-resource language dependency syntax is very scarce. This paper proposes a new method for analysing Mongolian dependency syntax. The seq2seq model structure is employed for dependency syntax analysis of Mongolian, taking advantage of its morphological features and grammatical structure. However, the scarcity of a Mongolian corpus and the numerous parameters of other deep learning networks can result in overfitting. To address this challenge, a two-stage self-training framework is employed in conjunction with a confidence dynamic threshold setting method to construct a corpus of Mongolian dependent syntax. The experimental results demonstrate that the scores of dependent syntactic analysis in Labeled Attachment Score (LAS) and the Unlabeled Attachment Score (UAS) reach 86.65% and 85.80%, respectively.

In recent years, deep learning is widely applied in surface defect detection. However, there are still challenges with low detection efficiency, often leading to false positives and false negatives. To address these issues, we propose a multi-scale convolution and multi-dimensional feature fusion detection model (MAF-DETR) based on RT-DETR. We designed the Dilated Gated Extraction Module (DGEM) to enhance multi-scale feature extraction and global information awareness. Additionally, we developed a Dynamic Feature Aggregation Module (DFAM) to improve the correlation between semantic and feature information, enhancing the ability to detect micro-texture variations. Furthermore, we optimized RepC3 with the C3K2 module for better feature fusion. Experimental results demonstrate that our model achieves mAP50and mAP50−95of 82% and 37.4% respectively on the RoboFlow wire rope defect dataset, surpassing the baseline model by 4.4% and 4.5%. On the NEU-DET dataset, the model attains mAP50and mAP50−95of 74.1% and 41.6%, demonstrating improvements of 5% and 2.8% over the baseline model.

Transfer learning improves task performance in sentiment analysis by borrowing knowledge from pre-trained models and source domains, but still faces challenges such as language differences, model overfitting, and scarcity of target data. Due to the lack of Mongolian sentiment corpora, cross-lingual sentiment analysis methods are difficult to achieve high accuracy. This paper first proposes a cross-lingual word embedding method based on isomorphic reinforcement projection, which focuses on the mapping relationship between languages with the same structure, rather than homographs and synonyms. By constructing a personalised adapter, the isomorphic offset of words in different languages is automatically learned, and the word embeddings calibrated by the offset are linearly projected to a shared space, thereby improving the problem of data sparsity. At the same time, in order to alleviate the problems of low efficiency in learning temporal relationships in traditional Convolutional Neural Network (CNN) methods, this paper proposes a Bidirectional Deep Temporal Convolution Network (BiDTCN) model for sentiment analysis of Mongolian. Time-series convolution is used to effectively retain sequence information, and both forward and backward directions are used to capture bidirectional semantic dependencies. The improved BiDTCN model significantly improves the accuracy of Mongolian sentiment analysis, especially when dealing with long text sequences. Compared with the traditional model, the accuracy is improved by 3.27%, which fully verifies the effectiveness of the model.

With the advancement of deep learning, surface defect detection has achieved remarkable progress. However, traditional detection methods employ single feature extraction strategies, leading to feature aliasing, information redundancy, and interference that fails to model complex non-linear feature dependencies. This reduces detection accuracy and increases error rates. We propose a Wavelet Domain Enhancement-Based Graph Guided Fusion Model for Defect Detection (WG-DETR). Our Anisotropic Direction Enhancement Unit (ADEU) enhances spatial features through optimized multi-directional filters, improving sensitivity to linear defects and robustness against non-homogeneous textures. The Wavelet Bidirectional Frequency Enhancement Unit (WBFU) uses Haar wavelets for frequency domain decomposition, creating bidirectional feature interaction channels that effectively isolate and enhance different frequency domain representations. Our Feature-similarity Graph-guided Unit (FGU) constructs graph topological structures for receptive field decoupling and joint modeling of local and global feature dependencies. Experimental results show WG-DETR achieves 82.4% and 35.7% on mAP50and mAP50−95metrics, improvements of 4.8% and 2.8% over the baseline.

The characteristics of neural machine translation require training and updating through a large number of corpus for hundreds of millions of parameters. In this situation, Mongolian Neural Machine Translation(MNMT) needs a variety of targeted training techniques and additional strategies to alleviate the problems caused by resource scarcity. These problems run through the whole translation process. taking the key steps of model training as a clue, This paper conducted detailed experiments and analysis on the main training content, and summarizes the relevant research and key issues according to the mainstream training processes such as ‘corpus processing→word embedding training→parameter pre-training→end-to-end model training→translation key problem analysis’. On this basis, this paper is committed to some long-standing stubborn problems to give a review of the treatment methods and training suggestions, and to provide some references for other researchers.

Conventional Neural Machine Translation (NMT) relies on previous tokens and the hidden state of the target for the inference of the target tokens in the decoding phase, and this left-to-right decoding approach loses the context of the target sequence from right-to-left. In addition, the vanilla attention mechanism lacks interactivity in the bilingual training phase, where the computation of the attention weights is independent at each step, which leads the decoder to disregard whether or not the current token has already been translated. In this paper, we proposed a novel interactive synchronized bi-directional inference method that uses past and future contexts to synchronously predict target sequences and incorporates Neural Turing Machine (NTM) ideas to detect historical attentional information, which relies on a read − write mechanism to update the source hidden state. We evaluate the proposed model in the WMT14 German-English translation task and the LDC Chinese-English translation task, and the experimental results show that our method improves 2.18 and 4.07 BLEU scores over the Transformer, respectively, which fully demonstrates the effectiveness of the proposed method.

Weak robustness and noise adaptability are major issues for Low-Resource Neural Machine Translation (NMT) models. Adversarial example is currently a major tool to improve model robustness and how to generate an adversarial examples that can degrade the performance of the model and ensure semantic consistency is a challenging task. In this paper, we adopt multi-reward reinforcement learning to generate adversarial examples for low-resource NMT. Specifically, utilizing gradient ascent to modify the source sentence, the discriminator and changes estimate are used to determine whether the generated adversarial examples maintain semantic consistency and the overall modifications of adversarial examples. Furthermore, we also install a language model reward to measure the fluency of adversarial examples. Experimental results on low-resource translation tasks show that our method highly aggressive to the model while maintaining semantic constraints greatly. Moreover, the model performance is significantly improved after fine-tuning with adversarial examples.

Multimodal Machine Translation (MMT) aims to enhance translation quality by incorporating information from other modalities (usually images). However, dominant MMT models do not consider that visual features not only provide supplementary information also introduce much noise. In this paper, we propose the visual features filter to solve this issue. Specifically, we adopt a soft-lookup function to select the visual features relevant to the text and then use these visual features as pseudo-words concatenating with a text representation. In addition, our model conducts two-pass decoding. The secondarypass decoding amounts to polishing which can identify errors in draft translations. The reason is that polishing expands the view in the process of decoding each target token, providing more contextual information. Besides, since most words in draft translations can be copied to final translations, we further equip our model with the copying mechanism to reserve those words that do not need to be corrected. MMT has achieved success in some mainstream languages at present. In order to promote the development of MMT in low-resource languages such as Mongolian, we deploy our model to the Mongolian→Chinese translation task. We expand Multi30k dataset to synthetic Mongolian and Chinese descriptions. Experiments on synthetic Mongolian and Chinese datasets demonstrate that our model can bring significant improvements.

By using document-level contextual information, document-level neural machine translation can achieve better results than ordinary machine translation, but traditional document-level machine translation is difficult to focus on the contextual sentence articulation relations and deep positional relations within the discourse while utilizing document-level vocabulary, and the model can concentrate only on relatively shallow inter-sentential relations or positional information. In this paper, we consider that most adjacent sentences are connected in document translation, and such links help improve the quality of translation. We propose a document translation model that focuses more on inter-sentential relations based on the previous work, and propose two methods to strengthen the model's positional information input, and combine these two methods to enhance the traditional Transformer positional information input. This paper also proposes a method for inserting paragraph information to allow inter-sentential relations to be learned by the model, and uses the improved Transformer model for Chinese-Mongolian document translation. Experiments show that in the improved Transformer system, the BLEU scores are enhanced on the Chinese-Mongolian machine translation task after fusing positional information and inter-sentential relation information, and the translation achieves better performance.

Reinforcement Learning (RL) has been proved to alleviate metric inconsistency and exposure deviation in training-evaluation of neural machine translation (NMT), but the sample efficiency is limited by sampling methods (Temporal-Difference (TD) or Monte-Carlo (MC)), and still cannot compensate for the inefficient non-zero rewards caused by insufficient data sets. In addition, RL rewards can only be effective when the model parameters are basically determined. Therefore, we proposed episodic control reinforcement learning method, which obtains the model with basically determined parameters through the knowledge transfer, and records the historical action trajectory by introducing semi-tabular differentiable neural dictionary (DND), the model can quickly approximate the real state-value according to samples reward when updating policy. We verified on CCMT2019 Mongolian-Chinese (Mo-Zh), Tibetan-Chinese (Ti-Zh), and Uyghur-Chinese (Ug-Zh) tasks, and the results showed that the quality was significantly improved, which fully demonstrated the effectiveness of the method.

Nearly all of the work in neural machine translation (NMT) is limited to a quite restricted vocabulary, crudely treating all other words the same as an < unk > symbol. For the translation of language with abundant morphology, unknown (UNK) words also come from the misunderstanding of the translation model to the morphological changes. In this study, we explore two ways to alleviate the UNK problem in NMT: a new generative adversarial network (added value constraints and semantic enhancement) and a preprocessing technique that mixes morphological noise. The training process is like a win-win game in which the players are three adversarial sub models (generator, filter, and discriminator). In this game, the filter is to emphasize the discriminator’s attention to the negative generations that contain noise and improve the training efficiency. Finally, the discriminator cannot easily discriminate the negative samples generated by the generator with filter and human translations. The experimental results show that the proposed method significantly improves over several strong baseline models across various language pairs and the newly emerged Mongolian-Chinese task is state-of-the-art.