@InProceedings{bhat-EtAl:2016:COLING,
  author    = {Bhat, Riyaz A.  and  Bhat, Irshad A.  and  Jain, Naman  and  Sharma, Dipti Misra},
  title     = {A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu},
  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     = {397--408},
  abstract  = {In Computational Linguistics, Hindi and Urdu are not viewed as a monolithic
	entity and have received separate attention with respect to their text
	processing. From part-of-speech tagging to machine translation, models are
	separately trained for both Hindi and Urdu despite the fact that they
	represent the same language. The reasons mainly are their divergent literary
	vocabularies and separate orthographies, and probably also their political
	status and the social perception that they are two separate languages. In this
	article, we propose a simple but efficient approach to bridge the lexical and
	orthographic differences between Hindi and Urdu texts. With respect to text
	processing, addressing the differences between the Hindi and Urdu texts would
	be beneficial in the following ways: (a) instead of training separate models,
	their individual resources can be augmented to train single, unified models for
	better generalization, and (b) their individual text processing applications
	can be used interchangeably under varied resource conditions.
	To remove the script barrier, we learn accurate statistical transliteration
	models which use sentence-level decoding to resolve word ambiguity. Similarly,
	we learn cross-register word embeddings from the harmonized Hindi and Urdu
	corpora to nullify their lexical divergences. As a proof of the concept, we
	evaluate our approach on the Hindi and Urdu dependency parsing under two
	scenarios: (a) resource sharing, and (b) resource augmentation. We demonstrate
	that a neural network-based dependency parser trained on augmented, harmonized
	Hindi and Urdu resources performs significantly better than the parsing models
	trained separately on the individual resources. We also show that we can
	achieve near state-of-the-art results when the parsers are used
	interchangeably.},
  url       = {http://aclweb.org/anthology/C16-1039}
}