@InProceedings{ghosh-EtAl:2017:Long,
  author    = {Ghosh, Sayan  and  Chollet, Mathieu  and  Laksana, Eugene  and  Morency, Louis-Philippe  and  Scherer, Stefan},
  title     = {Affect-LM: A Neural Language Model for Customizable Affective Text Generation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {634--642},
  abstract  = {Human verbal communication includes affective messages which are conveyed
	through use of emotionally colored words. There has been a lot of research
	effort in this direction but the problem of integrating state-of-the-art neural
	language models with affective information remains an area ripe for
	exploration. In this paper, we propose an extension to an LSTM (Long Short-Term
	Memory) language model for generation of conversational text, conditioned on
	affect categories. Our proposed model, Affect-LM enables us to customize the
	degree of emotional content in generated sentences through an additional design
	parameter. Perception studies conducted using Amazon Mechanical Turk show that
	Affect-LM can generate naturally looking emotional sentences without
	sacrificing grammatical correctness. Affect-LM also learns
	affect-discriminative word representations, and perplexity experiments show
	that additional affective information in conversational text can improve
	language model prediction.},
  url       = {http://aclweb.org/anthology/P17-1059}
}