@article{boleda_show_2016,
	title = {"{Show} me the cup": {Reference} with {Continuous} {Representations}},
	shorttitle = {"{Show} me the cup"},
	url = {http://arxiv.org/abs/1606.08777},
	abstract = {One of the most basic functions of language is to refer to objects in a shared scene. Modeling reference with continuous representations is challenging because it requires individuation, i.e., tracking and distinguishing an arbitrary number of referents. We introduce a neural network model that, given a definite description and a set of objects represented by natural images, points to the intended object if the expression has a unique referent, or indicates a failure, if it does not. The model, directly trained on reference acts, is competitive with a pipeline manually engineered to perform the same task, both when referents are purely visual, and when they are characterized by a combination of visual and linguistic properties.},
	urldate = {2017-03-30},
	journal = {arXiv:1606.08777 [cs]},
	author = {Boleda, Gemma and Padó, Sebastian and Baroni, Marco},
	month = jun,
	year = {2016},
	note = {arXiv: 1606.08777},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Learning},
}

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