@InProceedings{kordjamshidi-rahgooy-manzoor:2017:StructPred,
  author    = {Kordjamshidi, Parisa  and  Rahgooy, Taher  and  Manzoor, Umar},
  title     = {Spatial Language Understanding with Multimodal Graphs using Declarative Learning based Programming},
  booktitle = {Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {33--43},
  abstract  = {This work is on a previously formalized semantic evaluation task of spatial
	role labeling (SpRL) that aims at extraction of formal spatial meaning from
	text. Here, we report the results of initial efforts towards exploiting visual
	information in the form of images to help spatial language understanding. We
	discuss the way of designing new models in the framework of declarative
	learning-based programming (DeLBP). The DeLBP framework facilitates combining
	modalities and representing various data in a unified graph. 
	  The learning and inference models exploit the structure of the unified graph
	as well as the global first order domain constraints beyond the data to predict
	the semantics which forms a structured meaning representation of the spatial
	context. Continuous representations are used to relate the various elements of
	the graph originating from different modalities. We improved over the
	state-of-the-art results on SpRL.},
  url       = {http://www.aclweb.org/anthology/W17-4306}
}