@InProceedings{yadav-vig-shroff:2017:EACLlong,
  author    = {Yadav, Mohit  and  Vig, Lovekesh  and  Shroff, Gautam},
  title     = {Learning and Knowledge Transfer with Memory Networks for Machine Comprehension},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {850--859},
  abstract  = {Enabling machines to read and comprehend unstructured text remains an
	unfulfilled goal for NLP research. Recent research efforts on the ``machine
	comprehension'' task have managed to achieve close to ideal performance on
	simulated data. However, achieving similar levels of performance on small real
	world datasets has proved difficult; major challenges stem from the large
	vocabulary size, complex grammar, and, the frequent ambiguities in linguistic
	structure. On the other hand, the requirement of human generated annotations
	for training, in order to ensure a sufficiently diverse set of questions is
	prohibitively expensive. Motivated by these practical issues, we propose a
	novel curriculum inspired training procedure for Memory Networks to improve the
	performance for machine comprehension with relatively small volumes of training
	data. Additionally, we explore various training regimes for Memory Networks to
	allow knowledge transfer from a closely related domain having larger volumes of
	labelled data. We also suggest the use of a loss function to incorporate the
	asymmetric nature of knowledge transfer. Our experiments demonstrate
	improvements on Dailymail, CNN, and MCTest datasets.},
  url       = {http://www.aclweb.org/anthology/E17-1080}
}