Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Knowledge Aware Conversation Generation with Explainable Reasoning over
  Augmented Graphs   Two types of knowledge, triples from knowledge graphs and texts from
documents, have been studied for knowledge aware open-domain conversation
generation, in which graph paths can narrow down vertex candidates for
knowledge selection decision, and texts can provide rich information for
response generation. Fusion of a knowledge graph and texts might yield mutually
reinforcing advantages, but there is less study on that. To address this
challenge, we propose a knowledge aware chatting machine with three components,
an augmented knowledge graph with both triples and texts, knowledge selector,
and knowledge aware response generator. For knowledge selection on the graph,
we formulate it as a problem of multi-hop graph reasoning to effectively
capture conversation flow, which is more explainable and flexible in comparison
with previous work. To fully leverage long text information that differentiates
our graph from others, we improve a state of the art reasoning algorithm with
machine reading comprehension technology. We demonstrate the effectiveness of
our system on two datasets in comparison with state-of-the-art models.

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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: chem-ph
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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: q-bio