@InProceedings{calixto-EtAl:2017:EACLshort,
  author    = {Calixto, Iacer  and  Stein, Daniel  and  Matusov, Evgeny  and  Lohar, Pintu  and  Castilho, Sheila  and  Way, Andy},
  title     = {Using Images to Improve Machine-Translating E-Commerce Product Listings.},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {637--643},
  abstract  = {In this paper we study the impact of using images to machine-translate
	user-generated e-commerce product listings. We study how a multi-modal Neural
	Machine Translation (NMT) model compares to two text-only approaches: a
	conventional state-of-the-art attentional NMT and a Statistical Machine
	Translation (SMT) model. User-generated product listings often do not
	constitute grammatical or well-formed sentences. More often than not, they
	consist of the juxtaposition of short phrases or keywords. We train our models
	end-to-end as well as use text-only and multi-modal NMT models for re-ranking
	$n$-best lists generated by an SMT model. We qualitatively evaluate our
	user-generated training data also analyse how adding synthetic data impacts the
	results. We evaluate our models quantitatively using BLEU and TER and find that
	(i) additional synthetic data has a general positive impact on text-only and
	multi-modal NMT models, and that (ii) using a multi-modal NMT model for
	re-ranking n-best lists improves TER significantly across different n-best list
	sizes.},
  url       = {http://www.aclweb.org/anthology/E17-2101}
}