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To Batch or Not to Batch? Comparing Batching and Curriculum Learning Strategies across Tasks and Datasets

Author

Listed:
  • Laura Burdick

    (Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
    Current address: 2260 Hayward Street, Ann Arbor, MI 48109, USA.)

  • Jonathan K. Kummerfeld

    (Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA)

  • Rada Mihalcea

    (Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA)

Abstract

Many natural language processing architectures are greatly affected by seemingly small design decisions, such as batching and curriculum learning (how the training data are ordered during training). In order to better understand the impact of these decisions, we present a systematic analysis of different curriculum learning strategies and different batching strategies. We consider multiple datasets for three tasks: text classification, sentence and phrase similarity, and part-of-speech tagging. Our experiments demonstrate that certain curriculum learning and batching decisions do increase performance substantially for some tasks.

Suggested Citation

  • Laura Burdick & Jonathan K. Kummerfeld & Rada Mihalcea, 2021. "To Batch or Not to Batch? Comparing Batching and Curriculum Learning Strategies across Tasks and Datasets," Mathematics, MDPI, vol. 9(18), pages 1-11, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2234-:d:633613
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    Cited by:

    1. Florentina Hristea & Cornelia Caragea, 2022. "Preface to the Special Issue “Natural Language Processing (NLP) and Machine Learning (ML)—Theory and Applications”," Mathematics, MDPI, vol. 10(14), pages 1-5, July.

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