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Do Convolutional Networks need to be Deep for Text Classification ?

Hoa Le 1 Christophe Cerisara 1 Alexandre Denis 2
1 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : We study in this work the importance of depth in convolutional models for text classification, either when character or word inputs are considered. We show on 5 standard text classification and sentiment analysis tasks that deep models indeed give better performances than shallow networks when the text input is represented as a sequence of characters. However, a simple shallow-and-wide network outper-forms deep models such as DenseNet with word inputs. Our shallow word model further establishes new state-of-the-art performances on two datasets: Yelp Binary (95.9%) and Yelp Full (64.9%).
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Contributor : Christophe Cerisara <>
Submitted on : Sunday, February 24, 2019 - 7:53:24 AM
Last modification on : Thursday, March 11, 2021 - 2:26:02 PM
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  • HAL Id : hal-01690601, version 1


Hoa Le, Christophe Cerisara, Alexandre Denis. Do Convolutional Networks need to be Deep for Text Classification ?. AAAI Workshop on Affective Content Analysis, Feb 2018, New Orleans, United States. ⟨hal-01690601⟩



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