References
[1]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.
[2]. Peters M E, Neumann M, Iyyer M, et al. (2018). Deep contextualized word representations. arXiv:1802.05365.
[3]. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.
[4]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
[5]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., et al. (2017). Attention is all you need. Advances in neural information processing systems, 30.
[6]. Delobelle, P., Winters, T., & Berendt, B. (2020). Robbert: a dutch roberta-based language model. arXiv preprint arXiv:2001.06286.
[7]. Mnih, V., Heess, N., & Graves, A. (2014). Recurrent models of visual attention. Advances in neural information processing systems, 27.
[8]. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
[9]. Bengio, Y., Ducharme, R., & Vincent, P. (2000). A neural probabilistic language model. Advances in neural information processing systems, 13.
[10]. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
[11]. Farahani, M., Gharachorloo, M., Farahani, M., & Manthouri, M. (2021). Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters, 53(6), 3831-3847.
[12]. Niven, T., & Kao, H. Y. (2019). Probing neural network comprehension of natural language arguments. arXiv preprint arXiv:1907.07355.
[13]. McCoy, R. T., Pavlick, E., & Linzen, T. (2019). Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. arXiv preprint arXiv:1902.01007.
Cite this article
Bi,Y. (2023). Chinese news topic prediction using bidirectional encoder representation from transformers. Theoretical and Natural Science,18,133-139.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.
[2]. Peters M E, Neumann M, Iyyer M, et al. (2018). Deep contextualized word representations. arXiv:1802.05365.
[3]. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.
[4]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
[5]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., et al. (2017). Attention is all you need. Advances in neural information processing systems, 30.
[6]. Delobelle, P., Winters, T., & Berendt, B. (2020). Robbert: a dutch roberta-based language model. arXiv preprint arXiv:2001.06286.
[7]. Mnih, V., Heess, N., & Graves, A. (2014). Recurrent models of visual attention. Advances in neural information processing systems, 27.
[8]. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
[9]. Bengio, Y., Ducharme, R., & Vincent, P. (2000). A neural probabilistic language model. Advances in neural information processing systems, 13.
[10]. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
[11]. Farahani, M., Gharachorloo, M., Farahani, M., & Manthouri, M. (2021). Parsbert: Transformer-based model for persian language understanding. Neural Processing Letters, 53(6), 3831-3847.
[12]. Niven, T., & Kao, H. Y. (2019). Probing neural network comprehension of natural language arguments. arXiv preprint arXiv:1907.07355.
[13]. McCoy, R. T., Pavlick, E., & Linzen, T. (2019). Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. arXiv preprint arXiv:1902.01007.