Teaching Machines to Learn by Metaphors

Authors

  • Omer Levy Technion - Israel Institute of Technology
  • Shaul Markovitch Technion - Israel Institute of Technology

DOI:

https://doi.org/10.1609/aaai.v26i1.8278

Keywords:

Metaphor, Transfer Learning, Machine Learning, Classification, Regression

Abstract

Humans have an uncanny ability to learn new concepts with very few examples. Cognitive theories have suggested that this is done by utilizing prior experience of related tasks. We propose to emulate this process in machines, by transforming new problems into old ones. These transformations are called metaphors. Obviously, the learner is not given a metaphor, but must acquire one through a learning process. We show that learning metaphors yield better results than existing transfer learning methods. Moreover, we argue that metaphors give a qualitative assessment of task relatedness.

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Published

2021-09-20

How to Cite

Levy, O., & Markovitch, S. (2021). Teaching Machines to Learn by Metaphors. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 991-997. https://doi.org/10.1609/aaai.v26i1.8278

Issue

Section

AAAI Technical Track: Machine Learning