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        <title>Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets</title>
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    <abstract>Increasingly larger datasets have become a standard ingredient to advancing the state-of-the-art 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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.625</identifier>
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%0 Conference Proceedings
%T Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets
%A Bejan, Irina
%A Sokolov, Artem
%A Filippova, Katja
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F bejan-etal-2023-make
%X Increasingly larger datasets have become a standard ingredient to advancing the state-of-the-art 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.
%R 10.18653/v1/2023.emnlp-main.625
%U https://aclanthology.org/2023.emnlp-main.625
%U https://doi.org/10.18653/v1/2023.emnlp-main.625
%P 10107-10121

Markdown (Informal)

[Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets](https://aclanthology.org/2023.emnlp-main.625) (Bejan et al., EMNLP 2023)

• Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets (Bejan et al., EMNLP 2023)

ACL

• Irina Bejan, Artem Sokolov, and Katja Filippova. 2023. Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10107–10121, Singapore. Association for Computational Linguistics.