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Authors: Vanessa Bracamonte ; Seira Hidano ; Toru Nakamura and Shinsaku Kiyomoto

Affiliation: KDDI Research, Inc., Saitama, Japan

Keyword(s): Feature Importance Visualization, Text Classification Models, Model Evaluation, Case Studies.

Abstract: Visualization of explanations of text classification models is important for their evaluation. The evaluation of these models is mostly based on visualization techniques that apply to a datapoint level. Although a feature-level evaluation is possible with current visualization libraries, existing approaches do not yet implement ways for an evaluator to visualize how a text classification model behaves for features of interest for the whole data or a subset of it. In this paper, we describe and evaluate a simple feature-level approach that leverages existing interpretability methods and visualization techniques to provide evaluators information on the importance of specific features in the behavior of a text classification model. We conduct case studies of two types of text classification models: a movie review sentiment classification model and a comment toxicity model. The results show that a feature-level explanation visualization approach can help identify problems with the models.

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Paper citation in several formats:
Bracamonte, V.; Hidano, S.; Nakamura, T. and Kiyomoto, S. (2022). Feature-level Approach for the Evaluation of Text Classification Models. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 164-170. DOI: 10.5220/0010846900003124

@conference{ivapp22,
author={Vanessa Bracamonte. and Seira Hidano. and Toru Nakamura. and Shinsaku Kiyomoto.},
title={Feature-level Approach for the Evaluation of Text Classification Models},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP},
year={2022},
pages={164-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010846900003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP
TI - Feature-level Approach for the Evaluation of Text Classification Models
SN - 978-989-758-555-5
IS - 2184-4321
AU - Bracamonte, V.
AU - Hidano, S.
AU - Nakamura, T.
AU - Kiyomoto, S.
PY - 2022
SP - 164
EP - 170
DO - 10.5220/0010846900003124
PB - SciTePress

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