  Commonsense knowledge is crucial to many natural language processing tasks.
Existing works usually incorporate graph knowledge with conventional graph
neural networks (GNNs), leading to the text and graph knowledge encoding
processes being separated in a serial pipeline. We argue that these separate
representation learning stages may be suboptimal for neural networks to learn
the overall context contained in both types of input knowledge. In this paper,
we propose a novel context-aware graph-attention model (Context-aware GAT),
which can effectively incorporate global features of relevant knowledge graphs
based on a context-enhanced knowledge aggregation process. Specifically, our
framework leverages a novel representation learning approach to process
heterogeneous features - combining flattened graph knowledge with text. To the
best of our knowledge, this is the first attempt at hierarchically applying
graph knowledge aggregation on a connected subgraph in addition to contextual
information to support commonsense dialogue generation. This framework shows
superior performance compared to conventional GNN-based language frameworks.
Both automatic and human evaluation demonstrates that our proposed model has
significant performance uplifts over state-of-the-art baselines.

Computation and Language