@InProceedings{stratos:2017:SCLeM,
  author    = {Stratos, Karl},
  title     = {Reconstruction of Word Embeddings from Sub-Word Parameters},
  booktitle = {Proceedings of the First Workshop on Subword and Character Level Models in NLP},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {130--135},
  abstract  = {Pre-trained word embeddings improve the performance of a neural model at the
	cost of increasing the model size. We propose to benefit from this resource
	without paying the cost by operating strictly at the sub-lexical level. Our
	approach is quite simple: before task-specific training, we first optimize
	sub-word parameters to reconstruct pre-trained word embeddings using various
	distance measures. We report interesting results on a variety of tasks: word
	similarity, word analogy, and part-of-speech tagging.},
  url       = {http://www.aclweb.org/anthology/W17-4119}
}