Hierarchical Latent Alignment for Non-Autoregressive Generation under High Compression Ratio
Author:
Affiliation:
1. School of Computer Science and Technology, Harbin Institute of Technology
2. Beijing Langboat Technology Co., Ltd.
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transinf/E107.D/3/E107.D_2023EDP7111/_pdf
Reference41 articles.
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3. [3] J. Guo, X. Tan, L. Xu, T. Qin, E. Chen, and T.-Y. Liu, “Fine-tuning by curriculum learning for non-autoregressive neural machine translation,” Proc. AAAI Conference on Artificial Intelligence, pp.7839-7846, 2020. 10.1609/aaai.v34i05.6289
4. [4] W. Qi, Y. Gong, J. Jiao, Y. Yan, W. Chen, D. Liu, K. Tang, H. Li, J. Chen, R. Zhang, M. Zhou, and N. Duan, “Bang: Bridging autoregressive and non-autoregressive generation with large scale pretraining,” Proc. International Conference on Machine Learning, pp.8630-8639, 2021.
5. [5] P. Liu, C. Huang, and L. Mou, “Learning non-autoregressive models from search for unsupervised sentence summarization,” Proc. Annual Meeting of the Association for Computational Linguistics, pp.7916-7929, 2022. 10.18653/v1/2022.acl-long.545
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