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.

2023年1月13日 · This is an open-source toolkit for Heterogeneous Graph Neural Network based on DGL [Deep Graph Library] and PyTorch. We integrate SOTA models of heterogeneous …

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 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: …

【September 11, 2024】The modified code now supports the latest versions of torch and DGL. --Please run train.py to train the HHGNN in NYC city. Due to the huge number of hyperedges, …

这是在北邮 GAMMA LAB 实习期间精读的一篇 paper，原文链接为 HHGN: A Hierarchical Reasoning-based Heterogeneous Graph Neural Network for fact verification. 一、前置知识. Token：通常指的是文本中的一个单元，例如一个单词、字符或子词。例如，句子 “I love apples.”

hhgn模型结合了实体、句子以及上下文的多重特征，以实现证据的表示。 它利用异构图来捕捉这些特征的语义关系，这种方法特别适用于处理复杂的事实验证任务 。

In many intelligent transportation systems, predicting the future motion of heterogeneous traffic participants is a fundamental but challenging task due to various factors encompassing the agents’ dynamic states, interactions with neighboring agents and surrounding traffic infrastructures, and their stochastic and multi-modal natural behavior tendencies. However, existing approaches have ...