A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with
  Commonsense Knowledge
Kailai Yang Tianlin Zhang Shaoxiong Ji Sophia Ananiadou
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.