@InProceedings{gulati-agrawal:2017:RepEval,
  author    = {Gulati, Anmol  and  Agrawal, Kumar Krishna},
  title     = {Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games},
  booktitle = {Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {27--30},
  abstract  = {Acquiring language provides a ubiquitous mode of communication, across humans
	and robots. To this effect, distributional representations of words based on
	co-occurrence statistics, have provided significant advancements ranging across
	machine translation to comprehension. In this paper, we study the suitability
	of using general purpose word-embeddings for language learning in robots. We
	propose using text-based games as a proxy to evaluating word embedding on real
	robots. Based in a risk-reward setting, we review the effectiveness of the
	embeddings in navigating tasks in fantasy games, as an approximation to their
	performance on more complex scenarios, like language assisted robot navigation.},
  url       = {http://www.aclweb.org/anthology/W17-5305}
}