A cross-temporal contrastive disentangled model for ancient Chinese understanding.

Ancient Chinese is a crucial bridge for understanding Chinese history and culture. Most existing works utilize high-resource modern Chinese to understand low-resource ancient Chinese, but they fail to fully consider the semantic and syntactic gaps between them due to their changes over time, resulting in the misunderstanding of ancient Chinese. Hence, we propose a novel language pre-training framework for ancient Chinese understanding based on the Cross-temporal Contrastive Disentanglement Model (CCDM), which bridges the gap between modern and ancient Chinese with their parallel corpus. Specifically, we first explore a cross-temporal data augmentation method by disentangling and reconstructing the parallel ancient-modern corpus. It is noteworthy that the proposed decoupling strategy takes full account of the cross-temporal character between ancient and modern Chinese. Then, cross-temporal contrastive learning is exploited to train the model by fully leveraging the cross-temporal information. Finally, the trained language model is utilized for downstream tasks. We conduct extensive experiments on six ancient Chinese understanding tasks. Results demonstrate that our model outperforms the state-of-the-art baselines. Our framework also holds potential applicability to other languages that have undergone evolutionary changes, leading to shifts in syntax and semantics.1.

HG-PerCon: Cross-view contrastive learning for personality prediction.

Personality prediction task not only helps us to better understand personal needs and preferences but also is essential for many fields such as psychology and behavioral economics. Current personality prediction primarily focuses on discovering personality traits through user posts. Additionally, there are also methods that utilize psychological information to uncover certain underlying personality traits. Although significant progress has been made in personality prediction, we believe that current solutions still overlook the long-term sustainability of personality and are constrained by the challenge of capturing consistent personality-related clues across different views in a simple and efficient manner. To this end, we propose HG-PerCon, which utilizes user representations based on historical semantic information and psychological knowledge for cross-view contrastive learning. Specifically, we design a transformer-based module to obtain user representations with long-lasting personality-related information from their historical posts. We leverage a psychological knowledge graph which incorporates language styles to generate user representations guided by psychological knowledge. Additionally, we employ contrastive learning to capture the consistency of user personality-related clues across views. To evaluate the effectiveness of our model, and our approach achieved a reduction of 2%, 4%, and 6% in RMSE compared to the second-best baseline method.

DGTR: Dynamic graph transformer for rumor detection.

Social media rumors have the capacity to harm the public perception and the social progress. The news propagation pattern is a key clue for detecting rumors. Existing propagation-based rumor detection methods represent propagation patterns as a static graph structure. They simply consider the structure information of news distribution in social networks and disregard the temporal information. The dynamic graph is an effective modeling tool for both the structural and temporal information involved in the process of news dissemination. Existing dynamic graph representation learning approaches struggle to capture the long-range dependence of the structure and temporal sequence as well as the rich semantic association between full graph features and individual parts. We build a transformer-based dynamic graph representation learning approach for rumor identification DGTR to address the aforementioned challenges. We design a position embedding format for the graph data such that the original transformer model can be utilized for learning dynamic graph representations. The model can describe the structural long-range reliance between the dynamic graph nodes and the temporal long-range dependence between the temporal snapshots by employing a self-attention mechanism. In addition, the CLS token in transformer may model the rich semantic relationships between the complete graph and each subpart. Extensive experiments demonstrate the superiority of our model when compared to the state of the art.