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Computer Science > Artificial Intelligence

arXiv:2204.10357 (cs)
[Submitted on 21 Apr 2022]

Title:A Framework for Interactive Knowledge-Aided Machine Teaching

Authors:Karan Taneja, Harshvardhan Sikka, Ashok Goel
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Abstract:Machine Teaching (MT) is an interactive process where humans train a machine learning model by playing the role of a teacher. The process of designing an MT system involves decisions that can impact both efficiency of human teachers and performance of machine learners. Previous research has proposed and evaluated specific MT systems but there is limited discussion on a general framework for designing them. We propose a framework for designing MT systems and also detail a system for the text classification problem as a specific instance. Our framework focuses on three components i.e. teaching interface, machine learner, and knowledge base; and their relations describe how each component can benefit the others. Our preliminary experiments show how MT systems can reduce both human teaching time and machine learner error rate.
Comments: 8 pages, 4 figures
Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
Cite as: arXiv:2204.10357 [cs.AI]
  (or arXiv:2204.10357v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2204.10357

Submission history

From: Karan Taneja [view email]
[v1] Thu, 21 Apr 2022 18:30:55 UTC (192 KB)
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