Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling Semantics with Gated Graph Neural Networks for Knowledge Base
  Question Answering   The most approaches to Knowledge Base Question Answering are based on
semantic parsing. In this paper, we address the problem of learning vector
representations for complex semantic parses that consist of multiple entities
and relations. Previous work largely focused on selecting the correct semantic
relations for a question and disregarded the structure of the semantic parse:
the connections between entities and the directions of the relations. We
propose to use Gated Graph Neural Networks to encode the graph structure of the
semantic parse. We show on two data sets that the graph networks outperform all
baseline models that do not explicitly model the structure. The error analysis
confirms that our approach can successfully process complex semantic parses.

20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph