@InProceedings{passban-liu-way:2016:COLING,
  author    = {Passban, Peyman  and  Liu, Qun  and  Way, Andy},
  title     = {Enriching Phrase Tables for Statistical Machine Translation Using Mixed Embeddings},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2582--2591},
  abstract  = {The phrase table is considered to be the main bilingual resource for the
	phrase-based statistical machine translation (PBSMT) model. During translation,
	a source sentence is decomposed into several phrases. The best match of each
	source phrase is selected among several target-side counterparts within the
	phrase table, and processed by the decoder to generate a sentence-level
	translation. The best match is chosen according to several factors, including a
	set of bilingual features. PBSMT engines by default provide four probability
	scores in phrase tables which are considered as the main set of bilingual
	features. Our goal is to enrich that set of features, as a better feature set
	should yield better translations. We propose new scores generated by a
	Convolutional Neural Network (CNN) which indicate the semantic relatedness of
	phrase pairs. We evaluate our model in different experimental settings with
	different language pairs. We observe significant improvements when the proposed
	features are incorporated into the PBSMT pipeline.},
  url       = {http://aclweb.org/anthology/C16-1243}
}