Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Character-Level Decoder without Explicit Segmentation for Neural
  Machine Translation   The existing machine translation systems, whether phrase-based or neural,
have relied almost exclusively on word-level modelling with explicit
segmentation. In this paper, we ask a fundamental question: can neural machine
translation generate a character sequence without any explicit segmentation? To
answer this question, we evaluate an attention-based encoder-decoder with a
subword-level encoder and a character-level decoder on four language
pairs--En-Cs, En-De, En-Ru and En-Fi-- using the parallel corpora from WMT'15.
Our experiments show that the models with a character-level decoder outperform
the ones with a subword-level decoder on all of the four language pairs.
Furthermore, the ensembles of neural models with a character-level decoder
outperform the state-of-the-art non-neural machine translation systems on
En-Cs, En-De and En-Fi and perform comparably on En-Ru.

5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol