Deep Learning for Category-Free Grounded Language Acquisition
Links to Fileshttp://iral.cs.umbc.edu/Pubs/PillaiNAACLws2019.pdf
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Type of Work11 pages
conference papers and proceedings preprints
Citation of Original PublicationNisha Pillai , et.al, Deep Learning for Category-Free Grounded Language Acquisition, NAACL-SpLU-RoboNLP, Minneapolis, Minnesota, June 2019, http://iral.cs.umbc.edu/Pubs/PillaiNAACLws2019.pdf
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grounding acquisition system
logistic regression classifier
Interactive Robotics and Language Lab
We propose a learning system in which language is grounded in visual percepts without pre-defined category constraints. We present a unified generative method to acquire a shared semantic/visual embedding that enables a more general language grounding acquisition system. We evaluate the efficacy of this learning by predicting the semantics of ground truth objects and comparing the performance with each of a predefined category classifier and a simple logistic regression classifier. Our preliminary results suggest that this generative approach exhibits promising results in language grounding without pre-specifying visual categories such as color and shape.