Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Semantics in Neural Machine Translation with Graph
  Convolutional Networks   Semantic representations have long been argued as potentially useful for
enforcing meaning preservation and improving generalization performance of
machine translation methods. In this work, we are the first to incorporate
information about predicate-argument structure of source sentences (namely,
semantic-role representations) into neural machine translation. We use Graph
Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders
and achieve improvements in BLEU scores over the linguistic-agnostic and
syntax-aware versions on the English--German language pair.

26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con