@InProceedings{she-chai:2017:Long,
  author    = {She, Lanbo  and  Chai, Joyce},
  title     = {Interactive Learning of Grounded Verb Semantics towards Human-Robot Communication},
  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     = {1634--1644},
  abstract  = {To enable human-robot communication and collaboration, previous works represent
	grounded verb semantics as the potential change of state to the physical world
	caused by these verbs. Grounded verb semantics are acquired mainly based on the
	parallel data of the use of a verb phrase and its corresponding sequences of
	primitive actions demonstrated by humans. The
	rich interaction between teachers and students that is considered important in
	learning new skills has not yet been explored. To address this limitation, this
	paper presents a new interactive learning approach that allows robots to
	proactively engage in interaction with human partners by asking good questions
	to learn models for grounded verb semantics. The proposed approach uses
	reinforcement learning to allow the robot to acquire an optimal policy for its
	question-asking behaviors by maximizing the long-term reward. Our empirical
	results have shown that the interactive learning approach leads to more
	reliable models for grounded verb semantics, especially in the noisy
	environment which is full of uncertainties. Compared to previous work, the
	models acquired from interactive learning result in a 48% to 145% performance
	gain when applied in new situations.},
  url       = {http://aclweb.org/anthology/P17-1150}
}