References
[1]. Vaswani, Ashish, et al. “Attention is all you need”. Advances in neural information processing systems 30 (2017).
[2]. Subakti, A., Murfi, H. & Hariadi, N. “The performance of BERT as data representation of text clustering”. Journal of Big Data 9, 15 (2022).
[3]. Lin, Tianyang, et al. "A Survey of Transformers." Artificial Intelligence Open, 34 (2022). https://doi.org/10.1016/j.aiopen.2022.10.001.
[4]. Dai, Zihang, et al. "Transformer-xl: Attentive language models beyond a fixed-length context." arXiv preprint arXiv:1901.02860 (2019).
[5]. Yang, Zhilin, et al. "Xlnet: Generalized autoregressive pretraining for language understanding." Advances in neural information processing systems 32 (2019).
[6]. Lan, Zhenzhong, et al. "Albert: A lite bert for self-supervised learning of language representations." arXiv preprint arXiv:1909.11942 (2020).
[7]. Salehinejad, H., Sankar, S., Barfett, J., Colak, E., & Valaee, S. Recent Advances in Recurrent Neural Networks. arXiv preprint arXiv:1801.01078 (2017).
[8]. Bengio, Y., Simard, P., & Frasconi, P. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166 (1994).
[9]. Wadawadagi, Ramesh, and Veerappa Pagi. "Sentiment analysis with deep neural networks: comparative study and performance assessment." Artificial Intelligence Review 53.8: 6155-6195 (2020).
[10]. Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
[11]. Schneider, Phillip, et al. "A decade of knowledge graphs in natural language processing: A survey." arXiv preprint arXiv:2210.00105 (2022).
[12]. Wu, Ning, et al. "Large language models are diverse role-players for summarization evaluation." arXiv preprint arXiv:2303.15078 (2023).
[13]. Davison Joe. 2020a. “New Pipeline for Zero-Shot Text Classification.” Retrieved December 28, (2021).
Cite this article
Sun,Y. (2024). The evolution of transformer models from unidirectional to bidirectional in Natural Language Processing. Applied and Computational Engineering,42,281-289.
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]. Vaswani, Ashish, et al. “Attention is all you need”. Advances in neural information processing systems 30 (2017).
[2]. Subakti, A., Murfi, H. & Hariadi, N. “The performance of BERT as data representation of text clustering”. Journal of Big Data 9, 15 (2022).
[3]. Lin, Tianyang, et al. "A Survey of Transformers." Artificial Intelligence Open, 34 (2022). https://doi.org/10.1016/j.aiopen.2022.10.001.
[4]. Dai, Zihang, et al. "Transformer-xl: Attentive language models beyond a fixed-length context." arXiv preprint arXiv:1901.02860 (2019).
[5]. Yang, Zhilin, et al. "Xlnet: Generalized autoregressive pretraining for language understanding." Advances in neural information processing systems 32 (2019).
[6]. Lan, Zhenzhong, et al. "Albert: A lite bert for self-supervised learning of language representations." arXiv preprint arXiv:1909.11942 (2020).
[7]. Salehinejad, H., Sankar, S., Barfett, J., Colak, E., & Valaee, S. Recent Advances in Recurrent Neural Networks. arXiv preprint arXiv:1801.01078 (2017).
[8]. Bengio, Y., Simard, P., & Frasconi, P. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166 (1994).
[9]. Wadawadagi, Ramesh, and Veerappa Pagi. "Sentiment analysis with deep neural networks: comparative study and performance assessment." Artificial Intelligence Review 53.8: 6155-6195 (2020).
[10]. Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
[11]. Schneider, Phillip, et al. "A decade of knowledge graphs in natural language processing: A survey." arXiv preprint arXiv:2210.00105 (2022).
[12]. Wu, Ning, et al. "Large language models are diverse role-players for summarization evaluation." arXiv preprint arXiv:2303.15078 (2023).
[13]. Davison Joe. 2020a. “New Pipeline for Zero-Shot Text Classification.” Retrieved December 28, (2021).