Unsupervised Selection of Negative Examples for Grounded Language Learning
Permanent Link
Author/Creator
Author/Creator ORCID
Date
Type of Work
Department
Program
Citation of Original Publication
Nisha Pillai, Cynthia Matuszek, Unsupervised Selection of Negative Examples for Grounded Language Learning, Thirty-Second AAAI Conference on Artificial Intelligence , 2018, https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17440
Rights
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
Abstract
There has been substantial work in recent years on grounded language acquisition, in which language and sensor data are used to create a model relating linguistic constructs to the perceivable world. While powerful, this approach is frequently hindered by ambiguities, redundancies, and omissions found in natural language. We describe an unsupervised system that learns language by training visual classifiers, first selecting important terms from object descriptions, then automatically choosing negative examples from a paired corpus of perceptual and linguistic data. We evaluate the effectiveness of each stage as well as the system's performance on the overall learning task.