@InProceedings{gangal-EtAl:2017:EMNLP2017,
  author    = {Gangal, Varun  and  Jhamtani, Harsh  and  Neubig, Graham  and  Hovy, Eduard  and  Nyberg, Eric},
  title     = {Charmanteau: Character Embedding Models For Portmanteau Creation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2917--2922},
  abstract  = {Portmanteaus are a word formation phenomenon where two words combine into a new
	word. We propose character-level neural sequence-to-sequence (S2S) methods for
	the task of portmanteau generation that are end-to-end-trainable, language
	independent, and do not explicitly use additional phonetic information. We
	propose a noisy-channel-style model, which allows for the incorporation of
	unsupervised word lists, improving performance over a standard source-to-target
	model. This model is made possible by an exhaustive candidate generation
	strategy specifically enabled by the features of the portmanteau task.
	Experiments find our approach superior to a state-of-the-art FST-based baseline
	with respect to ground truth accuracy and human evaluation.},
  url       = {https://www.aclweb.org/anthology/D17-1315}
}