mlsanityData to 7 Oct 2026

A Framework for Interactive Knowledge-Aided Machine Teaching

arXiv 2204.10357 ↗First posted 21 Apr 2022cs.AI cs.HC cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 21 Apr 2022.

Builds on

10 of 25 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  4. 2020Learning to summarize from human feedbackcited by 2,562
  5. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  6. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  7. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  8. 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
  9. 2020A Survey of Active Learning for Text Classification using Deep Neural Networkscited by 50
  10. 2021Agent Smith: Teaching Question Answering to Jill Watsoncited by 4

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2.

3 papers.

  1. 2024GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agencycited by 18
  2. 2024Can Active Label Correction Improve LLM-based Modular AI Systems?cited by 4
  3. 2022Human-AI Interaction Design in Machine Teachingcited by 1