  As a branch of advanced artificial intelligence, dialogue systems are
prospering. Multi-turn response selection is a general research problem in
dialogue systems. With the assistance of background information and pre-trained
language models, the performance of state-of-the-art methods on this problem
gains impressive improvement. However, existing studies neglect the importance
of external commonsense knowledge. Hence, we design a Siamese network where a
pre-trained Language model merges with a Graph neural network (SinLG). SinLG
takes advantage of Pre-trained Language Models (PLMs) to catch the word
correlations in the context and response candidates and utilizes a Graph Neural
Network (GNN) to reason helpful common sense from an external knowledge graph.
The GNN aims to assist the PLM in fine-tuning, and arousing its related
memories to attain better performance. Specifically, we first extract related
concepts as nodes from an external knowledge graph to construct a subgraph with
the context response pair as a super node for each sample. Next, we learn two
representations for the context response pair via both the PLM and GNN. A
similarity loss between the two representations is utilized to transfer the
commonsense knowledge from the GNN to the PLM. Then only the PLM is used to
infer online so that efficiency can be guaranteed. Finally, we conduct
extensive experiments on two variants of the PERSONA-CHAT dataset, which proves
that our solution can not only improve the performance of the PLM but also
achieve an efficient inference.
