Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Xiankun Zhang,Qinhu Zhang Conference proce

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L. Claes,C. Burri,R. Neugebauer,U. Gruberstems based on social network graphs. Combining social network graphs with user-item graphs can capture dynamic preference features, achieving more accurate recommendations. However, user behavioral data often contains noise and is sparse, which may result in suboptimal model performance. To address

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L. Claes,C. Burri,R. Neugebauer,U. Grubere opportunity to fully leverage pre-trained knowledge from single-pass models. This practice leads to increased training cost and complexity. In this paper, we propose a unified two-pass decoding framework comprising three core modules: a pre-trained Visual Encoder, a pre-trained Draft Decoder, and

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L. Claes,C. Burri,R. Neugebauer,U. Gruberations from text. This task has become challenging when dealing with complex sentences that encompass overlapping sub-events. To address this issue, we propose a novel ontology-aware neural approach for extracting overlapping events. Our approach consists of an Ontology-Aware Semantic Encoder (OASE)