The context-aware emotional reasoning ability of AI systems, especially in conversations, is of vital importance in applications such as online opinion mining from social media and empathetic dialogue systems. Due to the implicit nature of conveying emotions in many scenarios, commonsense knowledge is widely utilized to enrich utterance semantics and enhance conversation modeling. However, most previous knowledge infusion methods perform empirical knowledge filtering and design highly customized architectures for knowledge interaction with the utterances, which can discard useful knowledge aspects and limit their generalizability to different knowledge sources. Based on these observations, we propose a Bipartite Heterogeneous Graph (BHG) method for enhancing emotional reasoning with commonsense knowledge. In BHG, the extracted context-aware utterance representations and knowledge representations are modeled as heterogeneous nodes. Two more knowledge aggregation node types are proposed to perform automatic knowledge filtering and interaction. BHG-based knowledge infusion can be directly generalized to multi-type and multi-grained knowledge sources. In addition, we propose a Multi-dimensional Heterogeneous Graph Transformer (MHGT) to perform graph reasoning, which can retain unchanged feature spaces and unequal dimensions for heterogeneous node types during inference to prevent unnecessary loss of information. Experiments show that BHG-based methods significantly outperform state-of-the-art knowledge infusion methods and show generalized knowledge infusion ability with higher efficiency. Further analysis proves that previous empirical knowledge filtering methods do not guarantee to provide the most useful knowledge information. Our code is available at: https://github.com/SteveKGYang/BHG.

翻译：上下文感知的情感推理能力，尤其是在对话场景中，对于社交媒体在线观点挖掘和共情对话系统等应用至关重要。由于许多场景中情感表达的隐含性，常识知识被广泛用于丰富话语语义并增强对话建模。然而，大多数先前的知识注入方法采用经验性知识过滤，并设计高度定制化的架构来实现知识与话语的交互，这可能会丢弃有用的知识维度，并限制其在不同知识源之间的泛化能力。基于这些观察，我们提出了一种二分异构图（BHG）方法，用于利用常识知识增强情感推理。在BHG中，提取的上下文感知话语表示和知识表示被建模为异质节点。我们提出了两种额外的知识聚合节点类型，以执行自动知识过滤和交互。基于BHG的知识注入可以直接泛化到多类型和多粒度的知识源。此外，我们提出了一种多维异构图Transformer（MHGT）来执行图推理，该方法在推理过程中可为异质节点类型保留不变的特征空间和不等的维度，以防止不必要的信息丢失。实验表明，基于BHG的方法显著优于最先进的知识注入方法，并以更高的效率展现出泛化的知识注入能力。进一步分析证明，先前的经验性知识过滤方法并不能保证提供最有用的知识信息。我们的代码地址为：https://github.com/SteveKGYang/BHG。