  In the research of end-to-end dialogue systems, using real-world knowledge to
generate natural, fluent, and human-like utterances with correct answers is
crucial. However, domain-specific conversational dialogue systems may be
incoherent and introduce erroneous external information to answer questions due
to the out-of-vocabulary issue or the wrong knowledge from the parameters of
the neural network. In this work, we propose PK-Chat, a Pointer network guided
Knowledge-driven generative dialogue model, incorporating a unified pretrained
language model and a pointer network over knowledge graphs. The words generated
by PK-Chat in the dialogue are derived from the prediction of word lists and
the direct prediction of the external knowledge graph knowledge. Moreover,
based on the PK-Chat, a dialogue system is built for academic scenarios in the
case of geosciences. Finally, an academic dialogue benchmark is constructed to
evaluate the quality of dialogue systems in academic scenarios and the source
code is available online.
