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.

Detecting electron microscope images of cashmere and wool fibers is challenging; traditional manual methods struggle to distinguish between them. Cashmere and wool fibers are extremely similar in scale, fiber length, and fineness, coupled with the scarcity of relevant resources caused by the exorbitant cost of obtaining electron microscope images. These factors are intertwined, making it also difficult for the accuracy of deep learning detection methods to reach an ideal level. In response to the preceding issue, this paper presents a detector capable of learning discriminative features between fibers from electron microscope images and effectively completing the detection task with limited data. Initially, to address the challenge of capturing the complex features of cashmere and wool, this paper introduces Wool-Linear Deformable Convolution (W-LDConv) derived from LDConv. Furthermore, to overcome issues such as the inability of deformable large-kernel attention to adapt to fiber distribution and the excessive number of parameters, W-LDConv is used to take over the role of deformable convolution, forming the Linear Deformable Feature Capture Module (LDFC). Ultimately, the LDFC module is integrated with the efficient and stable YOLO11 model to propose the LDFC-YOLO object detector. Compared with the baseline model, the precision, recall, and [email protected] have improved by 12.9%, 18.6%, and 13.9%, respectively.

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.

Neural Machine Translation (NMT) has achieved significant progress for high-resource language pairs but still faces challenges with low-resource pairs like Mongolian-Chinese. Mongolian presents unique grammatical and semantic modeling difficulties as an agglutinative language with rich morphology and SOV word order (Subject-Object-Verb). Taking Convolutional Neural Networks (CNNs) and Transformers as examples: CNNs struggle to capture long-range dependencies, while Transformers, despite their global modeling capabilities, impose high computational costs. Existing Mongolian-Chinese NMT methods often prioritize translation quality but overlook computational efficiency, limiting their applicability on resource-constrained devices. This paper proposes a lightweight model, Dynamic Convolution with Long-Range Attention (DCLA), which balances translation quality and efficiency. DCLA uses a recognizer module to analyze Mongolian-specific features, such as sentence length, structural complexity, and low-frequency word distribution. It effectively addresses syntactic challenges caused by SOV-SVO word order differences and semantic difficulties posed by polysemous and low-frequency words. DCLA adopts two strategies based on sample complexity: applying convolution for simpler samples to reduce computational cost and multi-head attention for complex samples to enhance modeling. Experiments demonstrate that DCLA outperforms the state-of-the-art Transformer model in Mongolian-Chinese translation, with a 2.7-point BLEU improvement, a 21% reduction in parameters, and a 40% decrease in computational cost.

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.

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.

With the increasing numbers of user-generated content on the web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Existing deep learning methods ignore that the sentiment polarity of the target is related to the entire text structure, and prevalent approaches among them cannot effectively use the syntactic information. In this paper, we present a deep learning model that employs graph neural networks and graph-based attention mechanisms for aspect based sentiment analysis. In our work, the given text is considered as a graph based on its syntactic structure and the target is the specific region of the graph. Structural attention model and graph attention model are used to concentrate on relations between words and certain regions of the graph. We conduct comprehensive experiments on publicly accessible datasets, and results demonstrate that our model outperforms the state-of-the-art baselines.

With the amount of user-generated information on the Web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Aspect based sentiment analysis has gained increasing attention in decade years, but it remains a daunting task. Recently, approaches based on recurrent neural networks and convolutional neural networks have shown competitive results in this field. However, they don't take fully account of the entire text structure and the relation between words in a given document. In this paper, we propose a novel neural network method to address this problem, in which the text is treated as a graph and the aspect is the specific area of the graph. For the first time, we apply graph convolutional neural networks and structural attention model to aspect based sentiment analysis. Experiments on public-available datasets demonstrate the efficiency and effectiveness of our model.