|
[1]B. Thormundsson, “Market size and revenue comparison for artificial intelligence worldwide from 2018 to 2030”, Available: https://www.statista.com/statistics/941835/artificial-intelligence-market-size-revenue-comparisons, Jun. 2022, Accessed Jul. 2023. [2]K. S. Jones, “Natural Language Processing: A Historical Review”, Current Issues in Computational Linguistics: In Honour of Don Walker, pp. 3-16, Springer, Dordrecht, 1994. [3]U. A. Shah, S. Yousaf, I. Ahmad, S. U. Rehman and M. O. Ahmad, “Accelerating Revised Simplex Method Using GPU-Based Basis Update”, IEEE Access, vol. 8, pp. 52121-52138, 2020. [4]A. Coates, B. Huval, T. Wang, D. Wu, B. Catanzaro and N. Andrew, “Deep learning with cots HPC systems”, Proceeding of International Conference on Machine Learning (ICML), pp. 1337-1345, 2013. [5]A. Krizhevsky, I. Sutskever and G. E. Hinton, “ImageNet classification with deep convolutional neural networks”, Proceeding of Advances in Neural Information Processing Systems (NIPS), pp. 1097-1105, 2012. [6]X. Zhang, J. Zhao and Y. LeCun, “Character-level convolutional networks for text classification”, Proceeding of Advances in Neural Information Processing Systems (NIPS), pp. 649-657, 2015. [7]Q. Xie, Z. Dai, E. Hovy, M. Luong and Q. V. Le, “Unsupervised data augmentation for consistency training”, arXiv:1904.12848, 2019, Accessed Jul. 2023. [8]Y. Zhang, G. Chen, D. Yu, K. Yaco, S. Khudanpur and J. Glass, “Highway long short-term memory RNNs for distant speech recognition”, Proceeding of International Conference on Acoustics, Speech, & Signal Processing (ICASSP), pp. 5755-5759, 2016. [9]I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, “Generative adversarial nets”, Proceeding of Advances in Neural Information Processing Systems (NIPS), pp. 2672-2680, 2014. [10]Z. Yang, Z. Hu, R. Salakhutdinov and T. Berg-Kirkpatrick, “Improved variational autoencoders for text modeling using dilated convolutions”. arXiv:1702.08139, 2017, Accessed Jul. 2023. [11]cavedu, “Data Augmentation 資料增強”, Available: https://blog.cavedu.com/2019/07/18/data-augmentation/, July 2019, Accessed Jul. 2023. [12]W. Y. Wang and D. Yang, “That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using #petpeeve tweets”, Proceeding of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2557-2563, 2015. [13]R. Sennrich, B. Haadow and A. Birch, “Improving neural machine translation models with monolingual data”, Proceeding of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 86-96, 2016. [14]H. Guo, Y. Mao and R. Zhang, “Augmenting Data with Mixup for Sentence Classification: An Empirical Study”, arXiv:1905.08941, 2019, Accessed Jul. 2023. [15]K. Jiang and X. Lu, “Natural Language Processing and Its Applications in Machine Translation: A Diachronic Review”, Proceeding of 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI), pp. 210–214, Jul. 2023. [16]G. Zhai, Y. Yang, H. Wang and S. Du, “Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data”, IEEE Access, vol. 3, pp. 311-319, 2020. [17]L. Liu et al., “Boost AI Power: Data Augmentation Strategies With Unlabeled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination With Electronic Nose”, IEEE Access, vol. 21, pp. 22995-23005, 2021. [18]A. Sethia; R. Patel; P. Raut, “Data Augmentation using Generative models for Credit Card Fraud Detection”, Proceeding of 2018 4th International Conference on Computing Communication and Automation (ICCCA), pp. 1-6, Accessed Jul. 2023. [19]J. Devlin, M.W. Chang, K. Lee and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, Proceeding of North American Chapter of the Association for Computational Linguistics (NAACL): Human Language Technologies, vol. 1, pp. 4171-4186, Jun. 2019. [20]A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser and I. Polosukhin, “Attention Is All You Need”, Proceeding of Advances in Neural Information Processing Systems, pages 6000–6010, 2017. [21]K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition”, Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016. [22]J. L. Ba, J. R. Kiros and G.E. Hinton, “Layer normalization”, arXiv:1607.06450, 2016, Accessed Jul. 2023. [23]adam-liu, “圖解Transformer”, Available: https://blog.csdn.net/qq_41664845/article/details/84969266, Dec. 2018, Accessed Jul. 2023. [24]N. T. M. Trang and M. Shcherbakov, “Vietnamese Question Answering System from Multilingual BERT Models to Monolingual BERT Model”, Proceeding of 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART), pp. 201-206, 2020. [25]C. C. Lee, Z. Gao and C. L. Tsai, “BERT-Based Stock Market Sentiment Analysis”, Proceeding of 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), pp. 1-2, 2020. [26]W. Chang, F. Du and Y. Wang, “Research on Malicious URL Detection Technology Based on BERT Model”, Proceeding of 2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN), pp. 340-345, 2021. [27]C. Raffel, N. Shazeer, A. RoBERTs, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li and P. J. Liu, “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”, Cornell University, Computer Science, Machine Learning, arXiv:1910. 10683, 2019, Accessed Jul. 2023. [28]J. Dodge, M. Sap, A. Marasović, W. Agnew, G. Ilharco, D. Groeneveld, M. Mitchell and M. Gardner, “Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus”, Proceeding of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 1286–1305, 2021. [29]A. Radford, J. Wu, R. Child, D. Luan, D. Amodei and I. Sutskever, “Language models are unsupervised multitask learners”, OpenAI Blog, vol. 1, pp. 8, 2019. [30]D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano and G. Irving, “Fine-tuning language models from human preferences”, arXiv:1909.08593, 2019, Accessed Jul. 2023. [31]N. S. Keskar, B. McCann, L. R. Varshney, C. Xiong and R. Socher, “CTRL: A Conditional Transformer Language Model for Controllable Generation”, arXiv:1909.05858, 2019, Accessed Jul. 2023. [32]Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov and E. P. Xing, “Toward controlled generation of text”, Proceeding of 34th International Conference on Machine Learning (ICML), pp. 1587-1596, 2017. [33]L. Yu, W. Zhang, J. Wang and Y. Yu, “Seqgan: Sequence generative adversarial nets with policy gradient”, Proceeding of Thirty-First AAAI Conference on Artificial Intelligence, pp. 2852–2858. 2017. [34]Y. Kikuchi, G. Neubig, R. Sasano, H. Takamura and M. Okumura, “Controlling output length in neural encoder-decoders”, Proceeding of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1328-1338, 2016. [35]J. Ficler and Y. Goldberg, “Controlling linguistic style aspects in neural language generation”, Workshop on Stylistic Variation, pp. 94-104, 2017. [36]J. Wei and K. Zou, “EDA: Easy data augmentation techniques for boosting performance on text classification tasks”, arXiv:1901.11196, 2019, Accessed Jul. 2023. [37]V. C. D. Hoang, P. Koehn, G. Haffari and Trevor Cohn, “Iterative Back-Translation for Neural Machine Translation”, Proceeding of the 2nd Workshop on Neural Machine Translation and Generation, pages 18–24, 2018. [38]S. Edunov, M. Ott, M. Auli and D. Grangier, “Understanding Back-Translation at Scale”, arXiv:1808.09381, 2018, Accessed Jul. 2023. [39]S. Prabhumoye, Y. Tsvetkov, R. Salakhutdinov and A. W Black, “Style Transfer Through Back-Translation”, arXiv:1804.09000, 2018, Accessed Jul. 2023. [40]aceimnorstuvwxz, “中文文本分類數據集”, Available: https://github.com/aceimnorstuvwxz/toutiao-text-classfication-dataset, May. 2021, Accessed Jul. 2023.
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