Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Neural Machine Translation   Models of neural machine translation are often from a discriminative family
of encoderdecoders that learn a conditional distribution of a target sentence
given a source sentence. In this paper, we propose a variational model to learn
this conditional distribution for neural machine translation: a variational
encoderdecoder model that can be trained end-to-end. Different from the vanilla
encoder-decoder model that generates target translations from hidden
representations of source sentences alone, the variational model introduces a
continuous latent variable to explicitly model underlying semantics of source
sentences and to guide the generation of target translations. In order to
perform efficient posterior inference and large-scale training, we build a
neural posterior approximator conditioned on both the source and the target
sides, and equip it with a reparameterization technique to estimate the
variational lower bound. Experiments on both Chinese-English and English-
German translation tasks show that the proposed variational neural machine
translation achieves significant improvements over the vanilla neural machine
translation baselines.

31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat