Building Language-Agnostic Grounded Language Learning Systems
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2019
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Pillai, N., Matuszek, C., Ferraro, F., & Kery, C. (2019). Building Language-Agnostic Grounded Language Learning Systems.
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© 2019 IEEE.
© 2019 IEEE.
Abstract
Learning the meaning of grounded language—
language that references a robot’s physical environment and
perceptual data—is an important and increasingly widely studied problem in robotics and human-robot interaction. However,
with a few exceptions, research in robotics has focused on
learning groundings for a single natural language pertaining
to rich perceptual data. We present experiments on taking
an existing natural language grounding system designed for
English and applying it to a novel multilingual corpus of
descriptions of objects paired with RGB-D perceptual data.
We demonstrate that this specific approach transfers well to
different languages, but also present possible design constraints
to consider for grounded language learning systems intended
for robots that will function in a variety of linguistic settings