@InProceedings{schulz-EtAl:2017:RepL4NLP,
  author    = {Schulz, Hannes  and  Zumer, Jeremie  and  El Asri, Layla  and  Sharma, Shikhar},
  title     = {A Frame Tracking Model for Memory-Enhanced Dialogue Systems},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {219--227},
  abstract  = {Recently, resources and tasks were proposed to go beyond state tracking in
	dialogue systems. An example is the frame tracking task, which requires
	recording multiple frames, one for each user goal set during the dialogue. This
	allows a user, for instance, to compare items corresponding to different goals.
	This paper proposes a model which takes as input the list of frames created so
	far during the dialogue, the current user utterance as well as the dialogue
	acts, slot types, and slot values associated with this utterance. The model
	then outputs the frame being referenced by each  triple of dialogue act, slot
	type, and slot value. We show that on the recently published Frames dataset,
	this model significantly outperforms a previously proposed rule-based baseline.
	In addition, we propose an extensive analysis of the frame tracking task by
	dividing it into sub-tasks and assessing their difficulty with respect to our
	model.},
  url       = {http://www.aclweb.org/anthology/W17-2626}
}