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Cited by 7Machine Learning and Data ClassificationNatural Language Processing TechniquesTopic Modeling

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Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets

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Bejan, Irina, Sokolov, Artem, Filippova, Katja·2023·Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
DOI 10.18653/v1/2023.emnlp-main.625
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Abstract

Increasingly larger datasets have become a standard ingredient to advancing the state-of-theart in NLP.However, data quality might have already become the bottleneck to unlock further gains.Given the diversity and the sizes of modern datasets, standard data filtering is not straight-forward to apply, because of the multifacetedness of the harmful data and elusiveness of filtering rules that would generalize across multiple tasks.We study the fitness of task-agnostic self-influence scores of training examples for data cleaning, analyze their efficacy in capturing naturally occurring outliers, and investigate to what extent self-influence based data cleaning can improve downstream performance in machine translation, question answering and text classification, building up on recent approaches to self-influence calculation and automated curriculum learning.

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Metadata

Authors
Bejan, I., Sokolov, A., & Filippova, K.
ORCID
Filippova, Katja
Venue
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics
Published
2023
Pages
10107-10121
DOI
10.18653/v1/2023.emnlp-main.625
OpenAlex
W4389519087
Cited by
7
References
6
Citations by year
2026: 1 · 2025: 2 · 2024: 1 · 2023: 3
FWCI
1.24
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
self-influence, noisy training data, data cleaning, data quality, outlier detection, machine translation, question answering, text classification

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CoChat. (2026). Citation check: Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets. https://app.cochat.ai/papers/10.18653/v1/2023.emnlp-main.625
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Bejan, I., Sokolov, A., & Filippova, K. (2023). Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2023.emnlp-main.625