2021年9月1日 · Our research goal in this paper is to develop a fact verification model which can leverage multi-granularity semantic units and capture their relations for evidence representation, as well as present the explainability in the evidence reasoning process. Hence, we propose a Hierarchical Reasoning-based Heterogeneous Fig. 1. 1.

2023年1月13日 · Easy-to-Use: OpenHGNN provides easy-to-use interfaces for running experiments with the given models and dataset. Besides, we also integrate optuna to get …

2021年9月1日 · HHGN combines multiple features of entity, sentence as well as context for evidence representation, and employs a heterogeneous graph to capture their semantic relations. Inspired by the human inference process, we design a hierarchical reasoning-based node updating strategy to propagate the evidence features.

2021年9月1日 · HHGN combines multiple features of entity, sentence as well as context for evidence representation, and employs a heterogeneous graph to capture their semantic relations. Inspired by the human inference process, we design a hierarchical reasoning-based node updating strategy to propagate the evidence features.

Paper: Hierarchical Heterogeneous Graph Network based Multimodal Emotion Recognition in Conversation. If you want to download the MELD database, you can access the link: …

2021年12月30日 · To facilitate robust and reproducible HGNN research, we construct the Heterogeneous Graph Benchmark (HGB), consisting of 11 diverse datasets with three tasks. HGB standardizes the process of heterogeneous graph data splits, feature processing, and performance evaluation.

2019年11月9日 · By weaving heterogeneous nodes into an integral unified graph, this hierarchical differentiation of node granularity enables HGN to support different question answering sub-tasks simultaneously. Experiments on the HotpotQA benchmark demonstrate that the proposed model achieves new state of the art, outperforming existing multi-hop QA approaches.

A heterogeneous graph is constructed that models the participant’s depression state and uses the graph attention network to aggregate the pieces of depressive clues and uses the focal loss as a loss function for dealing with class imbalance by reshaping the standard cross-entropy loss.

2024年9月16日 · 基于层次推理的异构图神经网络（HHGN）是一种用于事实验证的深度学习模型。 这种模型通过结合多个特征和层次推理策略，增强了对复杂证据的表示和处理能力。 HHGN 主要有以下特点： 1. **异构图结构**：HHGN 使用异构图来捕捉数据中的不同类型节点和边的关系。 异构图能够同时处理多种类型的信息，比如实体、句子和上下文，从而在验证事实时提供更全面的证据表示 [1] [2]。 2. **层次推理机制**：该模型采用了层次推理的方法来聚合节点特征。 这意 …

2021年9月1日 · HHGN combines multiple features of entity, sentence as well as context for evidence representation, and employs a heterogeneous graph to capture their semantic relations. Inspired by the...