research-article

Relevance-based Infilling for Natural Language Counterfactuals

Authors:

Francesco Bonchi,

Andreas KaltenbrunnerAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 88 - 98

https://doi.org/10.1145/3583780.3615029

Published: 21 October 2023 Publication History

Abstract

Counterfactual explanations are a natural way for humans to gain understanding and trust in the outcomes of complex machine learning algorithms. In the context of natural language processing, generating counterfactuals is particularly challenging as it requires the generated text to be fluent, grammatically correct, and meaningful. In this study, we improve the current state of the art for the generation of such counterfactual explanations for text classifiers. Our approach, named RELITC (Relevance-based Infilling for Textual Counterfactuals), builds on the idea of masking a fraction of text tokens based on their importance in a given prediction task and employs a novel strategy, based on the entropy of their associated probability distributions, to determine the infilling order of these tokens. Our method uses less time than competing methods to generate counterfactuals that require less changes, are closer to the original text and preserve its content better, while being competitive in terms of fluency. We demonstrate the effectiveness of the method on four different datasets and show the quality of its outcomes in a comparison with human generated counterfactuals.

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Cited By

Deng YGalib ATan PLuo LBaeza-Yates RBonchi F(2024)Unraveling Block Maxima Forecasting Models with Counterfactual ExplanationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671923(562-573)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671923

Index Terms

Relevance-based Infilling for Natural Language Counterfactuals
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Natural language generation

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
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Frank Hopfgartner
University of Koblenz, Germany
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Mark Lee
University of Birmingham, UK
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University of Birmingham, UK
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Program Chairs:
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Spotify, UK
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Min Zhang
Tsinghua University, China
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Federal University of Minas Gerais, Brazil

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Deng YGalib ATan PLuo LBaeza-Yates RBonchi F(2024)Unraveling Block Maxima Forecasting Models with Counterfactual ExplanationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671923(562-573)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671923

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