Multi-domain machine translation (MDMT) aims to build a unified model capable of translating content across diverse domains. Despite the impressive machine translation capabilities demonstrated by large language models (LLMs), domain adaptation still remains a challenge for LLMs. Existing MDMT methods such as in-context learning and parameter-efficient fine-tuning often suffer from domain shift, parameter interference and limited generalization. In this work, we propose a neuron-efficient fine-tuning framework for MDMT that identifies and updates consensus-aligned neurons within LLMs. These neurons are selected by maximizing the mutual information between neuron behavior and domain features, enabling LLMs to capture both generalizable translation patterns and domain-specific nuances. Our method then fine-tunes LLMs guided by these neurons, effectively mitigating parameter interference and domain-specific overfitting. Comprehensive experiments on three LLMs across ten German-English and Chinese-English translation domains evidence that our method consistently outperforms strong PEFT baselines on both seen and unseen domains, achieving state-of-the-art performance.

Community detection is a critical task in graph theory, social network analysis, and bioinformatics, where communities are defined as clusters of densely interconnected nodes.However, detecting communities in large-scale networks with millions of nodes and billions of edges remains challenging due to the inefficiency and unreliability of existing methods.Moreover, many current approaches are limited to specific graph types, such as unweighted or undirected graphs, reducing their broader applicability.To address these issues, we propose a novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the network's two-dimensional (2D) structural entropy within a potential game framework.In the game, nodes decide to stay in the current community or move to another based on a strategy that maximizes the 2D structural entropy utility function.Additionally, we introduce a structural entropy-based node overlapping heuristic for detecting overlapping communities, with a near-linear time complexity.Experimental results on real-world networks demonstrate that CoDeSEG is the fastest method available and achieves state-of-the-art performance in overlapping normalized mutual information (ONMI) and F1 scores.

With the evolution of information technology and media, the environment and carriers of fake news have undergone significant changes compared to the past, enabling the fabrication of user identities, social contexts of news, and other information. This poses a substantial challenge to traditional fake news detection techniques based on news content and user attributes. Consequently, researchers have explored methods utilizing the social context features of news for fake news detection. However, most approaches relying on propagation structures for detection employ only a single propagation feature, neglecting the importance of user feedback features for the global propagation structure. Additionally, the credibility of news publishers serves as critical prior information for assessing news authenticity, particularly in early detection stages. To address these limitations, this paper proposes a novel method that integrates the global propagation structure features of news with publisher credibility to capture discriminative information between real and fake news at an early propagation stage. Specifically, the method first designs a top-down forward propagation graph and a bottom-up reverse diffusion graph, using bidirectional graph convolutional networks to extract propagation features and feedback features, respectively, which are then aggregated into global structural features. Next, a structure-aware multi-head attention network is employed to predict publisher credibility, jointly optimizing the early fake news detection task. To validate the effectiveness of the proposed method, experiments are conducted on two public datasets. The results demonstrate that the proposed method outperforms existing approaches in accuracy, recall, and F1-score metrics. The code and data are available at https://github.com/dalianly/GSPC-master.

Chinese-Vietnamese cross-language event detection aims to cluster texts that describe the same events in Chinese and Vietnamese into corresponding event clusters. However, because Vietnamese is a low-resource language, directly using multilingual pre-trained models to align event representations in Chinese and Vietnamese texts yields suboptimal results, leading to poor performance in cross-lingual event detection. To address this challenge, we propose a method to enhance cross-lingual event detection between Chinese and Vietnamese by utilizing an aligned knowledge event graph. By leveraging aligned event knowledge, such as personal and place names, to establish correlations between events in different languages, we construct a cross-lingual aligned knowledge event graph. Under the constraint of relational associations, we use contrastive learning to model the similarities and differences between various events, making the representations of the same events in different languages more compact. This approach improves the model’s ability to represent Chinese-Vietnamese cross-lingual event texts and enhances the effectiveness of cross-lingual event detection. Experimental results demonstrate that our method, on multiple multilingual pre-trained models, achieves significant improvements across evaluation metrics such as normalized mutual information, adjusted normalized mutual information, and the adjusted rand coefficient.

Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. Moreover, these methods statically initialize node embedding before training, which, in turn, limits the ability to learn from message texts and relations simultaneously. In this article, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present \(\mathrm{RPLM}_{SED}\) ( R elational prompt-based P re-trained L anguage M odels for S ocial E vent D etection). We first propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences. Secondly, a new multi-relational prompt-based pairwise message learning mechanism is proposed to learn more comprehensive message representation from message pairs with multi-relational prompts using PLMs. Thirdly, we design a new clustering constraint to optimize the encoding process by enhancing intra-cluster compactness and inter-cluster dispersion, making the message representation more distinguishable. We evaluate the \(\mathrm{RPLM}_{SED}\) on three real-world datasets, demonstrating that the \(\mathrm{RPLM}_{SED}\) model achieves state-of-the-art performance in offline, online, low-resource, and long-tail distribution scenarios for social event detection tasks.

Large language models (LLMs) have demonstrated remarkable capabilities in comprehensively handling various types of natural language processing (NLP) tasks.However, there are significant differences in the knowledge and abilities required for different tasks.Therefore, it is important to understand whether the same LLM processes different tasks in the same way.Are there specific neurons in a LLM for different tasks?Inspired by neuroscience, this paper pioneers the exploration of whether distinct neurons are activated when a LLM handles different tasks.Compared with current research exploring the neurons of language and knowledge, task-specific neurons present a greater challenge due to their abstractness, diversity, and complexity.To address these challenges, this paper proposes a method for task-specific neuron localization based on Causal Gradient Variation with Special Tokens (CGVST).CGVST identifies task-specific neurons by concentrating on the most significant tokens during task processing, thereby eliminating redundant tokens and minimizing interference from non-essential neurons.Compared to traditional neuron localization methods, our approach can more effectively identify task-specific neurons.We conduct experiments across eight different public tasks.Experiments involving the inhibition and amplification of identified neurons demonstrate that our method can accurately locate task-specific neurons.

Opinion target classification of microblog comments is one of the most important tasks for public opinion analysis about an event. Due to the high cost of manual labeling, opinion target classification is generally considered as a weak-supervised task. This article attempts to address the opinion target classification of microblog comments through an event graph convolution network (EventGCN) in a weak-supervised manner. Specifically, we take microblog contents and comments as document nodes, and construct an event graph with three typical relationships of event microblogs, including the co-occurrence relationship of event keywords extracted from microblogs, the reply relationship of comments, and the document similarity. Finally, under the supervision of a small number of labels, both word features and comment features can be represented well to complete the classification. The experimental results on two event microblog datasets show that EventGCN can significantly improve the classification performance compared with other baseline models.

We present a simple, efficient data augmentation approach for boosting Chinese-Vietnamese neural machine translation performance by leveraging the linguistic difference between the two languages. We first define the formalized representation of modifier symmetry, which is one of the most representative linguistic differences between Chinese and Vietnamese. We then propose and test two data augmentation strategies for leveraging the linguistic difference, which can be integrated naturally with different translation models. Results indicate that both strategies can introduce linguistic rules to boost translation accuracy. Tests on Chinese-Vietnamese benchmarks show significant accuracy improvements. To facilitate studies in this domain, we also release an open-source toolkit 1 with flexible implementation for Chinese-Vietnamese linguistic difference tagging.

Summary Aspect‐category sentiment classification of microblog comments aims to identify the sentiment polarity of different opinion aspects in microblog comments, which is meaningful for the analysis of public opinion. At present, most of aspect‐category sentiment classification methods need much annotation data, and regard comments as independent samples, without using of the relationship between comments. This article proposes an aspect‐category sentiment classification method based on tensor graph convolutional networks. First, the combination of a comment and its aspect category is regarded as a hybrid node, and the original representation of a hybrid node is encoded by the Bert model. Second, sentiment graph and semantic graph are constructed according to the semantic similarity and sentimental relevance between hybrid nodes, and they are stacked into a tensor. Then two convolution operations, including intra‐graph convolution and inter‐graph convolution, are performed for each layer of graph tensor. In this way, hybrid nodes can learn and merge the heterogeneous information of different graphs. Finally, under the supervision of few labeled comments, the sentiment classification can be completed based on the features of the hybrid nodes. Experimental results on two microblog datasets show that the proposed model can significantly improve the performance of sentiment classification compared with other baseline models.