6 accepted papers
Affective computing has achieved notable success in recognizing explicit emotions from short, isolated dialogue segments. However, human emotions are often implicitly expressed, internally regulated, and dynamically evolve over extended interactions. Existing models struggle to disentangle internal
Rencent advancements in large language models (LLM) have shown impressive versatility across various tasks. Short text matching is one of the fundamental technologies in natural language processing. In previous studies, the common approach to applying them to Chinese is segmenting each sentence into
Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, th…
Heterogeneous Graph Neural Networks (HGNNs) aim to embed rich structural and semantic information of heterogeneous graphs into low-dimensional node representations. While HGNNs extend the foundational work of homogeneous Graph Neural Networks, the methodology for effectively transforming heterogeneo…
The powerful capability of HyperGraph Neural Networks (HGNNs) in modeling intricate, high-order relationships among multiple data samples stems primarily from their ability to aggregate both the direct neighborhood features of individual nodes and those associated with hyperedges. However, the limit…
Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the semantic relationship before and after the sentence after integ…