@InProceedings{feng-EtAl:2016:COLING3,
  author    = {Feng, Shi  and  Liu, Shujie  and  Yang, Nan  and  Li, Mu  and  Zhou, Ming  and  Zhu, Kenny Q.},
  title     = {Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation},
  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     = {3082--3092},
  abstract  = {In neural machine translation, the attention mechanism facilitates the
	translation process by producing a soft alignment between the source sentence
	and the target sentence.  However, without dedicated distortion and fertility
	models seen in traditional SMT systems, the learned alignment may not be
	accurate, which can lead to low translation quality. In this paper, we propose
	two novel models to improve attention-based neural machine translation.  We
	propose a recurrent attention mechanism as an implicit distortion model, and a
	fertility conditioned decoder as an implicit fertility model. We conduct
	experiments on large-scale Chinese--English translation tasks. The results
	show
	that our models significantly improve both the alignment and translation
	quality
	compared to the original attention mechanism and several other variations.},
  url       = {http://aclweb.org/anthology/C16-1290}
}