@InProceedings{schulz-aziz:2016:COLING,
  author    = {Schulz, Philip  and  Aziz, Wilker},
  title     = {Fast Collocation-Based Bayesian HMM Word Alignment},
  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     = {3146--3155},
  abstract  = {We present a new Bayesian HMM word alignment model for statistical machine
	translation. 
	The model is a mixture of an alignment model and a language model. 
	The alignment component is a Bayesian extension of the standard HMM. 
	The language model component is responsible for the generation of words needed
	for source fluency reasons from source language context. 
	This allows for untranslatable source words to remain unaligned and at the same
	time avoids the introduction of artificial NULL words which introduces
	unusually long alignment jumps. 
	Existing Bayesian word alignment models are unpractically slow because they
	consider each target position when resampling a given alignment link. 
	The sampling complexity therefore grows linearly in the target sentence length.
	In order to make our model useful in practice, we devise an auxiliary variable
	Gibbs sampler that allows us to resample alignment links in constant time
	independently of the target sentence length. 
	This leads to considerable speed improvements. 
	Experimental results show that our model performs as well as existing word
	alignment toolkits in terms of resulting BLEU score.},
  url       = {http://aclweb.org/anthology/C16-1296}
}