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        <title>From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information</title>
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        <namePart type="given">Hengru</namePart>
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    <abstract>Dropout is used to avoid overfitting by randomly dropping units from the neural networks during training. Inspired by dropout, this paper presents GI-Dropout, a novel dropout method integrating with global information to improve neural networks for text classification. Unlike the traditional dropout method in which the units are dropped randomly according to the same probability, we aim to use explicit instructions based on global information of the dataset to guide the training process. With GI-Dropout, the model is supposed to pay more attention to inapparent features or patterns. Experiments demonstrate the effectiveness of the dropout with global information on seven text classification tasks, including sentiment analysis and topic classification.</abstract>
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    <identifier type="doi">10.18653/v1/K18-1055</identifier>
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%0 Conference Proceedings
%T From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information
%A Xu, Hengru
%A Li, Shen
%A Hu, Renfen
%A Li, Si
%A Gao, Sheng
%Y Korhonen, Anna
%Y Titov, Ivan
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F xu-etal-2018-random
%X Dropout is used to avoid overfitting by randomly dropping units from the neural networks during training. Inspired by dropout, this paper presents GI-Dropout, a novel dropout method integrating with global information to improve neural networks for text classification. Unlike the traditional dropout method in which the units are dropped randomly according to the same probability, we aim to use explicit instructions based on global information of the dataset to guide the training process. With GI-Dropout, the model is supposed to pay more attention to inapparent features or patterns. Experiments demonstrate the effectiveness of the dropout with global information on seven text classification tasks, including sentiment analysis and topic classification.
%R 10.18653/v1/K18-1055
%U https://aclanthology.org/K18-1055
%U https://doi.org/10.18653/v1/K18-1055
%P 573-582

Markdown (Informal)

[From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information](https://aclanthology.org/K18-1055) (Xu et al., CoNLL 2018)

• From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information (Xu et al., CoNLL 2018)

ACL

• Hengru Xu, Shen Li, Renfen Hu, Si Li, and Sheng Gao. 2018. From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 573–582, Brussels, Belgium. Association for Computational Linguistics.