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Found 10 papers, 5 papers with code
Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue
Based on such a mechanism, we further propose an intention reasoning network (IR-Net), which consists of joint and multi-hop reasoning, to obtain intention-aware representations of conceptual tokens that can be used to capture the concept shifts involved in task-oriented conversations, so as to effectively identify user’s intention and generate more accurate responses.
UniTranSeR: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog System
Specifically, we first embed the multimodal features into a unified Transformer semantic space to prompt inter-modal interactions, and then devise a feature alignment and intention reasoning (FAIR) layer to perform cross-modal entity alignment and fine-grained key-value reasoning, so as to effectively identify user’s intention for generating more accurate responses.
GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification
Accurate fact verification depends on performing fine-grained reasoning over crucial entities by capturing their latent logical relations hidden in multiple evidence clues, which is generally lacking in existing fact verification models.
CMAL: A Novel Cross-Modal Associative Learning Framework for Vision-Language Pre-Training
With the flourishing of social media platforms, vision-language pre-training (VLP) recently has received great attention and many remarkable progresses have been achieved.
EventLens: Leveraging Event-Aware Pretraining and Cross-modal Linking Enhances Visual Commonsense Reasoning
First, by emulating the cognitive process of human reasoning, an Event-Aware Pretraining auxiliary task is introduced to better activate LLM's global comprehension of intricate scenarios.
DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation
To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data.
LGMRec: Local and Global Graph Learning for Multimodal Recommendation
The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e. g., purchases, clicks) and item various modalities (e. g., visual and textual).
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies
Specifically, for neighborhood-level dependencies, we explicitly consider both popularity score and preference correlation by designing a joint neighborhood-level dependency weight, based on which we construct a neighborhood-level dependencies graph to capture higher-order interaction features.
TopicVAE: Topic-aware Disentanglement Representation Learning for Enhanced Recommendation
However, most existing methods considering content information are not well-designed to disentangle user preference features due to neglecting the diversity of user preference on different semantic topics of items, resulting in sub-optimal performance and low interpretability.
A Light Heterogeneous Graph Collaborative Filtering Model using Textual Information
Afterward, by performing a simplified RGCN-based node information propagation on the constructed heterogeneous graph, the embeddings of users and items can be adjusted with textual knowledge, which effectively alleviates the negative effects of data sparsity.
