@inproceedings{quattoni-carreras-2020-comparison,
title = "A comparison between {CNN}s and {WFA}s for Sequence Classification",
author = "Quattoni, Ariadna and
Carreras, Xavier",
editor = "Moosavi, Nafise Sadat and
Fan, Angela and
Shwartz, Vered and
Glava{\v{s}}, Goran and
Joty, Shafiq and
Wang, Alex and
Wolf, Thomas",
booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sustainlp-1.21",
doi = "10.18653/v1/2020.sustainlp-1.21",
pages = "159--163",
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.",
}
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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)
ACL