Academic

How Much Noise Can BERT Handle? Insights from Multilingual Sentence Difficulty Detection

arXiv:2603.07346v1 Announce Type: new Abstract: Noisy training data can significantly degrade the performance of language-model-based classifiers, particularly in non-topical classification tasks. In this study we designed a methodological framework to assess the impact of denoising. More specifically, we explored a range of denoising strategies for sentence-level difficulty detection, using training data derived from document-level difficulty annotations obtained through noisy crowdsourcing. Beyond monolingual settings, we also address cross-lingual transfer, where a multilingual language model is trained in one language and tested in another. We evaluate several noise reduction techniques, including Gaussian Mixture Models (GMM), Co-Teaching, Noise Transition Matrices, and Label Smoothing. Our results indicate that while BERT-based models exhibit inherent robustness to noise, incorporating explicit noise detection can further enhance performance. For our smaller dataset, GMM-based n

N
Nouran Khallaf, Serge Sharoff · · 1 min read · 14 views
#cs.CL

arXiv:2603.07346v1 Announce Type: new Abstract: Noisy training data can significantly degrade the performance of language-model-based classifiers, particularly in non-topical classification tasks. In this study we designed a methodological framework to assess the impact of denoising. More specifically, we explored a range of denoising strategies for sentence-level difficulty detection, using training data derived from document-level difficulty annotations obtained through noisy crowdsourcing. Beyond monolingual settings, we also address cross-lingual transfer, where a multilingual language model is trained in one language and tested in another. We evaluate several noise reduction techniques, including Gaussian Mixture Models (GMM), Co-Teaching, Noise Transition Matrices, and Label Smoothing. Our results indicate that while BERT-based models exhibit inherent robustness to noise, incorporating explicit noise detection can further enhance performance. For our smaller dataset, GMM-based noise filtering proves particularly effective in improving prediction quality by raising the Area-Under-the-Curve score from 0.52 to 0.92, or to 0.93 when de-noising methods are combined. However, for our larger dataset, the intrinsic regularisation of pre-trained language models provides a strong baseline, with denoising methods yielding only marginal gains (from 0.92 to 0.94, while a combination of two denoising methods made no contribution). Nonetheless, removing noisy sentences (about 20\% of the dataset) helps in producing a cleaner corpus with fewer infelicities. As a result we have released the largest multilingual corpus for sentence difficulty prediction: see https://github.com/Nouran-Khallaf/denoising-difficulty

Executive Summary

This study assesses the impact of denoising on BERT-based models for sentence-level difficulty detection in multilingual settings. The authors evaluate various noise reduction techniques, including Gaussian Mixture Models, Co-Teaching, Noise Transition Matrices, and Label Smoothing. Results indicate that while BERT exhibits inherent robustness to noise, incorporating explicit noise detection can enhance performance. The study highlights the importance of data quality in non-topical classification tasks and the potential benefits of denoising methods in improving prediction quality. The authors also release a large multilingual corpus for sentence difficulty prediction, contributing to the field of natural language processing.

Key Points

  • ▸ BERT-based models exhibit inherent robustness to noise
  • ▸ Explicit noise detection can enhance performance
  • ▸ Denoising methods are particularly effective in improving prediction quality for smaller datasets
  • ▸ Pre-trained language models provide a strong baseline for larger datasets

Merits

Strength in methodology

The study employs a comprehensive methodological framework to assess the impact of denoising on BERT-based models, providing a robust evaluation of various noise reduction techniques.

Contribution to NLP field

The authors release a large multilingual corpus for sentence difficulty prediction, contributing to the advancement of natural language processing and machine learning research.

Insights into data quality

The study highlights the importance of data quality in non-topical classification tasks, emphasizing the need for effective denoising methods to improve prediction quality.

Demerits

Limitation in generalizability

The study's findings may not be generalizable to other domains or tasks, as the results are specific to sentence-level difficulty detection and BERT-based models.

Potential overemphasis on denoising

The study may overemphasize the importance of denoising methods, potentially overlooking other factors that contribute to model performance.

Expert Commentary

This study provides a valuable contribution to the field of natural language processing, highlighting the importance of data quality and the potential benefits of denoising methods in improving prediction quality. However, the study's findings may not be generalizable to other domains or tasks, and the potential overemphasis on denoising methods may overlook other factors that contribute to model performance. The release of a large multilingual corpus for sentence difficulty prediction is a significant contribution to the field, and the study's findings have practical implications for the development of BERT-based models.

Recommendations

  • ✓ Future studies should evaluate the generalizability of the study's findings to other domains and tasks.
  • ✓ Researchers should consider the potential limitations of relying solely on denoising methods to improve model performance.

Sources

Related Articles