Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Semantic Parsing over Multiple Knowledge-bases   A fundamental challenge in developing semantic parsers is the paucity of
strong supervision in the form of language utterances annotated with logical
form. In this paper, we propose to exploit structural regularities in language
in different domains, and train semantic parsers over multiple knowledge-bases
(KBs), while sharing information across datasets. We find that we can
substantially improve parsing accuracy by training a single
sequence-to-sequence model over multiple KBs, when providing an encoding of the
domain at decoding time. Our model achieves state-of-the-art performance on the
Overnight dataset (containing eight domains), improves performance over a
single KB baseline from 75.6% to 79.6%, while obtaining a 7x reduction in the
number of model parameters.

25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess