@InProceedings{kohn-baumann:2016:COLING,
  author    = {K\"{o}hn, Arne  and  Baumann, Timo},
  title     = {Predictive Incremental Parsing Helps Language Modeling},
  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     = {268--277},
  abstract  = {Predictive incremental parsing produces syntactic representations of
	sentences as they are produced, e.g. by typing or speaking.  In order
	to generate connected parses for such unfinished sentences, upcoming
	word types can be hypothesized and structurally integrated with
	already realized words.  For example, the presence of a determiner as
	the last word of a sentence prefix may indicate that a noun will
	appear somewhere in the completion of that sentence, and the
	determiner can be attached to the predicted noun.  We combine the
	forward-looking parser predictions with backward-looking N-gram
	histories and analyze in a set of experiments the impact on language
	models, i.e. stronger discriminative power but also higher data
	sparsity.  Conditioning N-gram models, MaxEnt models or RNN-LMs on
	parser predictions yields perplexity reductions of about 6%.  Our
	method (a) retains online decoding capabilities and (b) incurs
	relatively little computational overhead which sets it apart from
	previous approaches that use syntax for language modeling.  Our method
	is particularly attractive for modular systems that make use of a
	syntax parser anyway, e.g. as part of an understanding pipeline where
	predictive parsing improves language modeling at no additional cost.},
  url       = {http://aclweb.org/anthology/C16-1026}
}