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Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities.
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Y. Li, C. Yu, G. Sun, H. Jiang, F. Sun, W. Zu, Y. Wen, Y. Yang, and J. Wang, “Cross-utterance conditioned vae for non-autoregressive text-to-speech,” in ACL , 2022, pp. 391–400
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2022
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A. Q. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. Mcgrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” in ICML . PMLR, 2022, pp. 16 784–16 804
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2022
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Y. Wang and Z. Zhao, “Fastlts: Non-autoregressive end-to-end unconstrained lip-to-speech synthesis,” in ACM MM , 2022, pp. 5678–5687
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2022
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F. Huang, T. Tao, H. Zhou, L. Li, and M. Huang, “On the learning of non-autoregressive transformers,” in ICML . PMLR, 2022, pp. 9356–9376
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T. Komatsu, “Non-autoregressive asr with self-conditioned folded encoders,” in ICASSP . IEEE, 2022, pp. 7427–7431
2022
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2022
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N. Savinov, J. Chung, M. Binkowski, E. Elsen, and A. van den Oord, “Step-unrolled denoising autoencoders for text generation,” in ICLR , 2023
2023
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Z. Ma, C. Shao, S. Gui, M. Zhang, and Y. Feng, “Fuzzy alignments in directed acyclic graph for non-autoregressive machine translation,” in ICLR , 2023
2023
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J. W. Yoon, B. J. Woo, S. Ahn, H. Lee, and N. S. Kim, “Inter-kd: Intermediate knowledge distillation for ctc-based automatic speech recognition,” in SLT . IEEE, 2023, pp. 280–286
2023
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K.-H. Lu and K.-Y. Chen, “A context-aware knowledge transferring strategy for ctc-based asr,” in SLT . IEEE, 2023, pp. 60–67
2023
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S. Dingliwal, M. Sunkara, S. Ronanki, J. Farris, K. Kirchhoff, and S. Bodapati, “Personalization of ctc speech recognition models,” in SLT . IEEE, 2023, pp. 302–309
2023
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G. Liu, Z. Yang, T. Tao, X. Liang, J. Bao, Z. Li, X. He, S. Cui, and Z. Hu, “Don’t take it literally: An edit-invariant sequence loss for text generation,” in NAACL-HLT , 2022, pp. 2055–2078
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