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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 14: AAAI-21 Technical Tracks 14

Label Confusion Learning to Enhance Text Classification Models

February 1, 2023

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Authors

Biyang Guo

AI Lab, School of Information Management and Engineering, Shanghai University of Finance and Economics


Songqiao Han

AI Lab, School of Information Management and Engineering, Shanghai University of Finance and Economics


Xiao Han

AI Lab, School of Information Management and Engineering, Shanghai University of Finance and Economics


Hailiang Huang

AI Lab, School of Information Management and Engineering, Shanghai University of Finance and Economics


Ting Lu

AI Lab, School of Information Management and Engineering, Shanghai University of Finance and Economics


DOI:

10.1609/aaai.v35i14.17529


Abstract:

Representing the true label as one-hot vector is the common practice in training text classification models. However, the one-hot representation may not adequately reflect the relation between the instance and labels, as labels are often not completely independent and instances may relate to multiple labels in practice. The inadequate one-hot representations tend to train the model to be over-confident, which may result in arbitrary prediction and model overfitting, especially for confused datasets (datasets with very similar labels) or noisy datasets (datasets with labeling errors). While training models with label smoothing can ease this problem in some degree, it still fails to capture the realistic relation among labels. In this paper, we propose a novel Label Confusion Model (LCM) as an enhancement component to current popular text classification models. LCM can learn label confusion to capture semantic overlap among labels by calculating the similarity between instance and labels during training and generate a better label distribution to replace the original one-hot label vector, thus improving the final classification performance. Extensive experiments on five text classification benchmark datasets reveal the effectiveness of LCM for several widely used deep learning classification models. Further experiments also verify that LCM is especially helpful for confused or noisy datasets and superior to the label smoothing method.

Topics: AAAI

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HOW TO CITE:

Biyang Guo||Songqiao Han||Xiao Han||Hailiang Huang||Ting Lu Label Confusion Learning to Enhance Text Classification Models Proceedings of the AAAI Conference on Artificial Intelligence (2021) 12929-12936.

Biyang Guo||Songqiao Han||Xiao Han||Hailiang Huang||Ting Lu Label Confusion Learning to Enhance Text Classification Models AAAI 2021, 12929-12936.

Biyang Guo||Songqiao Han||Xiao Han||Hailiang Huang||Ting Lu (2021). Label Confusion Learning to Enhance Text Classification Models. Proceedings of the AAAI Conference on Artificial Intelligence, 12929-12936.

Biyang Guo||Songqiao Han||Xiao Han||Hailiang Huang||Ting Lu. Label Confusion Learning to Enhance Text Classification Models. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.12929-12936.

Biyang Guo||Songqiao Han||Xiao Han||Hailiang Huang||Ting Lu. 2021. Label Confusion Learning to Enhance Text Classification Models. "Proceedings of the AAAI Conference on Artificial Intelligence". 12929-12936.

Biyang Guo||Songqiao Han||Xiao Han||Hailiang Huang||Ting Lu. (2021) "Label Confusion Learning to Enhance Text Classification Models", Proceedings of the AAAI Conference on Artificial Intelligence, p.12929-12936

Biyang Guo||Songqiao Han||Xiao Han||Hailiang Huang||Ting Lu, "Label Confusion Learning to Enhance Text Classification Models", AAAI, p.12929-12936, 2021.

Biyang Guo||Songqiao Han||Xiao Han||Hailiang Huang||Ting Lu. "Label Confusion Learning to Enhance Text Classification Models". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.12929-12936.

Biyang Guo||Songqiao Han||Xiao Han||Hailiang Huang||Ting Lu. "Label Confusion Learning to Enhance Text Classification Models". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 12929-12936.

Biyang Guo||Songqiao Han||Xiao Han||Hailiang Huang||Ting Lu. Label Confusion Learning to Enhance Text Classification Models. AAAI[Internet]. 2021[cited 2023]; 12929-12936.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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101, Palo Alto, California 94303 All Rights Reserved

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