Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Question Answering over Incomplete KBs with Knowledge-Aware
  Reader   We propose a new end-to-end question answering model, which learns to
aggregate answer evidence from an incomplete knowledge base (KB) and a set of
retrieved text snippets. Under the assumptions that the structured KB is easier
to query and the acquired knowledge can help the understanding of unstructured
text, our model first accumulates knowledge of entities from a question-related
KB subgraph; then reformulates the question in the latent space and reads the
texts with the accumulated entity knowledge at hand. The evidence from KB and
texts are finally aggregated to predict answers. On the widely-used KBQA
benchmark WebQSP, our model achieves consistent improvements across settings
with different extents of KB incompleteness.

37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex