Titlebook: Database Systems for Advanced Applications; 27th International C Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki Conference proceedings

bypass

Open-Domain Dialogue Generation Grounded with Dynamic Multi-form Knowledge Fusionnsense knowledge graph to get apposite triples as 2nd hop. To merge these two forms of knowledge into the dialogue effectively, we design a dynamic virtual knowledge selector and a controller that help to enrich and expand knowledge space. Moreover, DMKCM adopts a novel dynamic knowledge memory modu

lanugo

lobster

迎合

Aligning Internal Regularity and External Influence of Multi-granularity for Temporal Knowledge Grapxternal random perturbation. Finally, according to the above obtained multi-granular information of rich features, ARIM-TE conducts alignment for them in both structure and semantics. Experimental results show that ARIM-TE outperforms current state-of-the-art KGE models on several TKG link predictio

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被詛咒的人

homocysteine

SimEmotion: A Simple Knowledgeable Prompt Tuning Method for Image Emotion Classificationnd . are introduced to enrich text semantics, forming knowledgeable prompts and avoiding considerable bias introduced by fixed designed prompts, further improving the model’s ability to distinguish emotion categories. Evaluations on four widely-used affective datasets, namely, Flickr and Instagram (

HALO

起草

Hanging on to the Imperial Pastand images and generate texts. It also involves cross-modal learning to enhance interactions between images and texts. The experiments verify our method in appropriateness, informativeness, and emotion consistency.

幸福愉悅感

https://doi.org/10.1007/978-3-031-35411-3ension. Moreover, we design two auxiliary tasks to implicitly capture the sentiment trend and key events lie in the context. The auxiliary tasks are jointly optimized with the primary story ending generation task in a multi-task learning strategy. Extensive experiments on the ROCStories Corpus show