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    <name type="personal">
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            <namePart type="given">Marie-Francine</namePart>
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    <abstract>In order to alleviate the huge demand for annotated datasets for different tasks, many recent natural language processing datasets have adopted automated pipelines for fast-tracking usable data. However, model training with such datasets poses a challenge because popular optimization objectives are not robust to label noise induced in the annotation generation process. Several noise-robust losses have been proposed and evaluated on tasks in computer vision, but they generally use a single dataset-wise hyperparamter to control the strength of noise resistance. This work proposes novel instance-adaptive training frameworks to change single dataset-wise hyperparameters of noise resistance in such losses to be instance-wise. Such instance-wise noise resistance hyperparameters are predicted by special instance-level label quality predictors, which are trained along with the main classification models. Experiments on noisy and corrupted NLP datasets show that proposed instance-adaptive training frameworks help increase the noise-robustness provided by such losses, promoting the use of the frameworks and associated losses in NLP models trained with noisy data.</abstract>
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%0 Conference Proceedings
%T Instance-adaptive training with noise-robust losses against noisy labels
%A Jin, Lifeng
%A Song, Linfeng
%A Xu, Kun
%A Yu, Dong
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F jin-etal-2021-instance
%X In order to alleviate the huge demand for annotated datasets for different tasks, many recent natural language processing datasets have adopted automated pipelines for fast-tracking usable data. However, model training with such datasets poses a challenge because popular optimization objectives are not robust to label noise induced in the annotation generation process. Several noise-robust losses have been proposed and evaluated on tasks in computer vision, but they generally use a single dataset-wise hyperparamter to control the strength of noise resistance. This work proposes novel instance-adaptive training frameworks to change single dataset-wise hyperparameters of noise resistance in such losses to be instance-wise. Such instance-wise noise resistance hyperparameters are predicted by special instance-level label quality predictors, which are trained along with the main classification models. Experiments on noisy and corrupted NLP datasets show that proposed instance-adaptive training frameworks help increase the noise-robustness provided by such losses, promoting the use of the frameworks and associated losses in NLP models trained with noisy data.
%R 10.18653/v1/2021.emnlp-main.457
%U https://aclanthology.org/2021.emnlp-main.457
%U https://doi.org/10.18653/v1/2021.emnlp-main.457
%P 5647-5663

Markdown (Informal)

[Instance-adaptive training with noise-robust losses against noisy labels](https://aclanthology.org/2021.emnlp-main.457) (Jin et al., EMNLP 2021)

• Instance-adaptive training with noise-robust losses against noisy labels (Jin et al., EMNLP 2021)

ACL

• Lifeng Jin, Linfeng Song, Kun Xu, and Dong Yu. 2021. Instance-adaptive training with noise-robust losses against noisy labels. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5647–5663, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.