Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Character-based Decoding Using Target-Side Morphological
  Information for Neural Machine Translation   Recently, neural machine translation (NMT) has emerged as a powerful
alternative to conventional statistical approaches. However, its performance
drops considerably in the presence of morphologically rich languages (MRLs).
Neural engines usually fail to tackle the large vocabulary and high
out-of-vocabulary (OOV) word rate of MRLs. Therefore, it is not suitable to
exploit existing word-based models to translate this set of languages. In this
paper, we propose an extension to the state-of-the-art model of Chung et al.
(2016), which works at the character level and boosts the decoder with
target-side morphological information. In our architecture, an additional
morphology table is plugged into the model. Each time the decoder samples from
a target vocabulary, the table sends auxiliary signals from the most relevant
affixes in order to enrich the decoder's current state and constrain it to
provide better predictions. We evaluated our model to translate English into
German, Russian, and Turkish as three MRLs and observed significant
improvements.

24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics