  End-to-end task-oriented dialogue systems aim to generate system responses
directly from plain text inputs. There are two challenges for such systems: one
is how to effectively incorporate external knowledge bases (KBs) into the
learning framework; the other is how to accurately capture the semantics of
dialogue history. In this paper, we address these two challenges by exploiting
the graph structural information in the knowledge base and in the dependency
parsing tree of the dialogue. To effectively leverage the structural
information in dialogue history, we propose a new recurrent cell architecture
which allows representation learning on graphs. To exploit the relations
between entities in KBs, the model combines multi-hop reasoning ability based
on the graph structure. Experimental results show that the proposed model
achieves consistent improvement over state-of-the-art models on two different
task-oriented dialogue datasets.
