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        <title>Convolutions Are All You Need (For Classifying Character Sequences)</title>
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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            <namePart type="given">Wei</namePart>
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            <namePart type="given">Alan</namePart>
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        <name type="personal">
            <namePart type="given">Tim</namePart>
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    <abstract>While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, the longer sequences make RNNs a poor choice. Convolutional neural networks (CNNs), although somewhat less ubiquitous than RNNs, have an internal structure more appropriate for long-distance character dependencies. To better understand how CNNs and RNNs differ in handling long sequences, we use them for text classification tasks in several character-level social media datasets. The CNN models vastly outperform the RNN models in our experiments, suggesting that CNNs are superior to RNNs at learning to classify character-level data.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6127</identifier>
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%0 Conference Proceedings
%T Convolutions Are All You Need (For Classifying Character Sequences)
%A Wood-Doughty, Zach
%A Andrews, Nicholas
%A Dredze, Mark
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F wood-doughty-etal-2018-convolutions
%X While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, the longer sequences make RNNs a poor choice. Convolutional neural networks (CNNs), although somewhat less ubiquitous than RNNs, have an internal structure more appropriate for long-distance character dependencies. To better understand how CNNs and RNNs differ in handling long sequences, we use them for text classification tasks in several character-level social media datasets. The CNN models vastly outperform the RNN models in our experiments, suggesting that CNNs are superior to RNNs at learning to classify character-level data.
%R 10.18653/v1/W18-6127
%U https://aclanthology.org/W18-6127
%U https://doi.org/10.18653/v1/W18-6127
%P 208-213

Markdown (Informal)

[Convolutions Are All You Need (For Classifying Character Sequences)](https://aclanthology.org/W18-6127) (Wood-Doughty et al., WNUT 2018)

• Convolutions Are All You Need (For Classifying Character Sequences) (Wood-Doughty et al., WNUT 2018)

ACL

• Zach Wood-Doughty, Nicholas Andrews, and Mark Dredze. 2018. Convolutions Are All You Need (For Classifying Character Sequences). In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 208–213, Brussels, Belgium. Association for Computational Linguistics.