@InProceedings{venugopal-rus:2016:COLING,
  author    = {Venugopal, Deepak  and  Rus, Vasile},
  title     = {Joint Inference for Mode Identification in Tutorial Dialogues},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2000--2011},
  abstract  = {Identifying dialogue acts and dialogue modes during tutorial interactions is an
	extremely crucial sub-step in understanding patterns of effective tutor-tutee
	interactions. In this work, we develop a novel joint inference method that
	labels each utterance in a tutoring dialogue session with a dialogue act and a
	specific mode from a set of pre-defined dialogue acts and modes, respectively.
	Specifically, we develop our joint model using Markov Logic Networks (MLNs), a
	framework that combines first-order logic with probabilities, and is thus
	capable of representing complex, uncertain knowledge. We define first-order
	formulas in our MLN that encode the inter-dependencies between dialogue modes
	and more fine-grained dialogue actions. We then use a joint inference to
	jointly label the modes as well as the dialogue acts in an utterance. We
	compare our system against a pipeline system based on SVMs on a real-world
	dataset with tutoring sessions of over 500 students. Our results show that the
	joint inference system is far more effective than the pipeline system in mode
	detection, and improves over the performance of the pipeline system by about 6
	points in F1 score. The joint inference system also performs much better than
	the pipeline system in the context of labeling modes that highlight important
	pedagogical steps in tutoring.},
  url       = {http://aclweb.org/anthology/C16-1188}
}