Abstract
Densely Connected Convolutional Networks (DenseNet) have demonstrated impressive performance on image classification tasks, but limited research has been conducted on using character-level DenseNet (char-DenseNet) architectures for text classification tasks. It is not clear what DenseNet architectures are optimal for text classification tasks. The iterative task of designing, training and testing of char-DenseNets is a time consuming task that requires expert domain knowledge. Evolutionary deep learning (EDL) has been used to automatically design CNN architectures for the image classification domain, thereby mitigating the need for expert domain knowledge. This study demonstrates the first work on using EDL to evolve char-DenseNet architectures for text classification tasks. A novel genetic programming-based algorithm (GP-Dense) coupled with an indirect-encoding scheme, facilitates the evolution of performant char-DenseNet architectures. The algorithm is evaluated on two popular text datasets, and the best-evolved models are benchmarked against four current state-of-the-art character-level CNN and DenseNet models. Results indicate that the algorithm evolves performant models for both datasets that outperform two of the state-of-the-art models in terms of model accuracy and three of the state-of-the-art models in terms of parameter size.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge (2016)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, NIPS 2015, vol. 1, pp. 649–657. MIT Press, Cambridge (2015)
Conneau, A., Schwenk, H., Barrault, L., Lecun, Y.: Very deep convolutional networks for text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, Valencia, Spain, pp. 1107–1116. Association for Computational Linguistics (April 2017). https://www.aclweb.org/anthology/E17-1104
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(76), 2493–2537 (2011). http://jmlr.org/papers/v12/collobert11a.html
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 5753–5763. Curran Associates, Inc. (2019). https://proceedings.neurips.cc/paper/2019/file/dc6a7e655d7e5840e66733e9ee67cc69-Paper.pdf
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423
Le, H.T., Cerisara, C., Denis, A.: Do Convolutional Networks need to be deep for text classification? In: AAAI Workshop on Affective Content Analysis. New Orleans, United States (February 2018)
Church, K.W.: Word2Vec. Nat. Lang. Eng. 23(1), 155–162 (2017)
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532–1543. Association for Computational Linguistics (October 2014). https://www.aclweb.org/anthology/D14-1162
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back-Propagating Errors, pp. 696–699. MIT Press, Cambridge (1988)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA (1992)
De Sa, C., Feldman, M., Ré, C., Olukotun, K.: Understanding and optimizing asynchronous low-precision stochastic gradient descent. In: 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA), pp. 561–574 (2017)
Hara, K., Saito, D., Shouno, H.: Analysis of function of rectified linear unit used in deep learning. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, CoRR abs/1512.0 (2015)
Liang, J., Meyerson, E., Hodjat, B., Fink, D., Mutch, K., Miikkulainen, R.: Evolutionary neural automl for deep learning. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, pp. 401–409. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3321707.3321721
Miikkulainen, R., et al.: Evolving deep neural networks (2017)
Wulczyn, E., Thain, N., Dixon, L.: Wikipedia talk labels: personal attacks (2017). https://figshare.com/articles/dataset/Wikipedia_Talk_Labels_Personal_Attacks/4054689
Gruau, F.: Neural Network Synthesis Using Cellular Encoding and the Genetic Algorithm. Ph.D. Thesis (1994)
Kaiming, H., Xiangyu, Z., Shaoqing, R., Jian, S.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification Kaiming. Biochem. Biophys. Res. Commun. 498(1), 254–261 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Londt, T., Gao, X., Andreae, P. (2021). Evolving Character-Level DenseNet Architectures Using Genetic Programming. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-72699-7_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72698-0
Online ISBN: 978-3-030-72699-7
eBook Packages: Computer ScienceComputer Science (R0)