Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Multi-modal Neural Machine Translation   Unsupervised neural machine translation (UNMT) has recently achieved
remarkable results with only large monolingual corpora in each language.
However, the uncertainty of associating target with source sentences makes UNMT
theoretically an ill-posed problem. This work investigates the possibility of
utilizing images for disambiguation to improve the performance of UNMT. Our
assumption is intuitively based on the invariant property of image, i.e., the
description of the same visual content by different languages should be
approximately similar. We propose an unsupervised multi-modal machine
translation (UMNMT) framework based on the language translation cycle
consistency loss conditional on the image, targeting to learn the bidirectional
multi-modal translation simultaneously. Through an alternate training between
multi-modal and uni-modal, our inference model can translate with or without
the image. On the widely used Multi30K dataset, the experimental results of our
approach are significantly better than those of the text-only UNMT on the 2016
test dataset.

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The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th