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Combining Unsupervised and Supervised Learning for Sample Efficient Continuous Language Grounding.

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  • 1Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium.

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Summary
This summary is machine-generated.

This study introduces a novel hybrid framework for artificial agents to learn natural language grounding. It effectively combines unsupervised and supervised learning, improving accuracy and efficiency with or without human guidance.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Machine Learning

Background:

  • Effective human-AI communication hinges on natural language understanding.
  • The Symbol Grounding Problem highlights the need to link words to perceptual information.
  • Existing grounding methods are either supervised (requiring tutors) or unsupervised (independent).

Purpose of the Study:

  • To propose a hybrid grounding framework combining supervised and unsupervised learning.
  • To enable continuous, open-ended learning without explicit training phases.
  • To leverage tutor support when available, while still learning independently.

Main Methods:

  • Developed a hybrid grounding framework integrating supervised and unsupervised learning paradigms.
  • Designed for continuous, open-ended learning.
  • Evaluated through two grounding scenarios, comparing against a state-of-the-art unsupervised Bayesian approach.

Main Results:

  • The proposed unsupervised grounding mechanism surpassed the baseline in accuracy, transparency, and deployability.
  • Combining supervised and unsupervised learning enhanced sample-efficiency and accuracy compared to purely unsupervised methods.
  • The framework demonstrated the ability to learn correct mappings even without supervision.

Conclusions:

  • Hybrid grounding frameworks offer a powerful approach to natural language understanding for artificial agents.
  • The proposed method provides a flexible and robust solution for grounding, adaptable to varying levels of available support.
  • This work advances the field by creating more capable and adaptable AI communication systems.