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More Sustainable Text Classification via Uncertainty Sampling and a Human-in-the-Loop

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Agents and Artificial Intelligence (ICAART 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13786))

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Abstract

Text classification by large deep learning networks achieves high accuracy, but uses a lot of computing power and requires high resource investment. In the age of climate change, sustainable solutions are sought that can attain acceptable accuracy with less resource investment. In this paper, we investigate lightweight text classifiers and combine them with a human-in-the-loop approach. Our solution identifies instances that are uncertain to classify and assigns them preferentially to a human domain expert. Experiments performed with nine classifiers on six datasets show that with manually labelling less than 30%, an F1-score between \(\sim \)95% and 99% is achievable for five of six datasets. The inference on energy-efficient GPU-less infrastructure with only 4 GB can be done in less than 1 s.

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Notes

  1. 1.

    https://scikit-learn.org/stable/index.html.

  2. 2.

    https://www.tensorflow.org/.

  3. 3.

    https://huggingface.co/bert-base-uncased.

  4. 4.

    https://huggingface.co/sentence-transformers/all-mpnet-base-v2.

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Authors and Affiliations

  1. Department of Computer Science, Hamburg University of Applied Sciences, Hamburg, Germany

    Jakob Smedegaard Andersen & Olaf Zukunft

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  1. Jakob Smedegaard Andersen
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  2. Olaf Zukunft
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Correspondence to Jakob Smedegaard Andersen .

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Editors and Affiliations

  1. LIACC/FEUP, Porto, Portugal

    Ana Paula Rocha

  2. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

  3. Leiden Institute of Advanced Computer Science, Leiden, The Netherlands

    Jaap van den Herik

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Andersen, J.S., Zukunft, O. (2022). More Sustainable Text Classification via Uncertainty Sampling and a Human-in-the-Loop. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2022. Lecture Notes in Computer Science(), vol 13786. Springer, Cham. https://doi.org/10.1007/978-3-031-22953-4_9

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  • DOI: https://doi.org/10.1007/978-3-031-22953-4_9

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