@InProceedings{lynn-EtAl:2017:EMNLP2017,
  author    = {Lynn, Veronica  and  Son, Youngseo  and  Kulkarni, Vivek  and  Balasubramanian, Niranjan  and  Schwartz, H. Andrew},
  title     = {Human Centered NLP with User-Factor Adaptation},
  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     = {1146--1155},
  abstract  = {We pose the general task of user-factor adaptation -- adapting supervised
	learning models to real-valued user factors inferred from a background of their
	language, reflecting the idea that a piece of text should be understood within
	the context of the user that wrote it. We introduce a continuous adaptation
	technique, suited for real-valued user factors that are common in social
	science and bringing us closer to personalized NLP, adapting to each user
	uniquely. We apply this technique with known user factors including age,
	gender, and personality traits, as well as latent factors, evaluating over five
	tasks: POS tagging, PP-attachment, sentiment analysis, sarcasm detection, and
	stance detection. Adaptation provides statistically significant benefits for 3
	of the 5 tasks: up to +1.2 points for PP-attachment, +3.4 points for sarcasm,
	and +3.0 points for stance.},
  url       = {https://www.aclweb.org/anthology/D17-1119}
}