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        <title>A comparison between CNNs and WFAs for Sequence Classification</title>
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        <namePart type="given">Ariadna</namePart>
        <namePart type="family">Quattoni</namePart>
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    <name type="personal">
        <namePart type="given">Xavier</namePart>
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            <title>Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Nafise</namePart>
            <namePart type="given">Sadat</namePart>
            <namePart type="family">Moosavi</namePart>
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        <name type="personal">
            <namePart type="given">Angela</namePart>
            <namePart type="family">Fan</namePart>
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        <name type="personal">
            <namePart type="given">Vered</namePart>
            <namePart type="family">Shwartz</namePart>
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            <namePart type="given">Goran</namePart>
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            <namePart type="given">Shafiq</namePart>
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        <name type="personal">
            <namePart type="given">Alex</namePart>
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            <namePart type="given">Thomas</namePart>
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    <abstract>We compare a classical CNN architecture for sequence classification involving several convolutional and max-pooling layers against a simple model based on weighted finite state automata (WFA). Each model has its advantages and disadvantages and it is possible that they could be combined. However, we believe that the first research goal should be to investigate and understand how do these two apparently dissimilar models compare in the context of specific natural language processing tasks. This paper is the first step towards that goal. Our experiments with five sequence classification datasets suggest that, despite the apparent simplicity of WFA models and training algorithms, the performance of WFAs is comparable to that of the CNNs.</abstract>
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    <identifier type="doi">10.18653/v1/2020.sustainlp-1.21</identifier>
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%0 Conference Proceedings
%T A comparison between CNNs and WFAs for Sequence Classification
%A Quattoni, Ariadna
%A Carreras, Xavier
%Y Moosavi, Nafise Sadat
%Y Fan, Angela
%Y Shwartz, Vered
%Y Glavaš, Goran
%Y Joty, Shafiq
%Y Wang, Alex
%Y Wolf, Thomas
%S Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F quattoni-carreras-2020-comparison
%X We compare a classical CNN architecture for sequence classification involving several convolutional and max-pooling layers against a simple model based on weighted finite state automata (WFA). Each model has its advantages and disadvantages and it is possible that they could be combined. However, we believe that the first research goal should be to investigate and understand how do these two apparently dissimilar models compare in the context of specific natural language processing tasks. This paper is the first step towards that goal. Our experiments with five sequence classification datasets suggest that, despite the apparent simplicity of WFA models and training algorithms, the performance of WFAs is comparable to that of the CNNs.
%R 10.18653/v1/2020.sustainlp-1.21
%U https://aclanthology.org/2020.sustainlp-1.21
%U https://doi.org/10.18653/v1/2020.sustainlp-1.21
%P 159-163

Markdown (Informal)

[A comparison between CNNs and WFAs for Sequence Classification](https://aclanthology.org/2020.sustainlp-1.21) (Quattoni & Carreras, sustainlp 2020)

• A comparison between CNNs and WFAs for Sequence Classification (Quattoni & Carreras, sustainlp 2020)

ACL

• Ariadna Quattoni and Xavier Carreras. 2020. A comparison between CNNs and WFAs for Sequence Classification. In Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing, pages 159–163, Online. Association for Computational Linguistics.