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                     Proc. of the 2017 Conf. on Empirical Methods in Natural Language Processing. Copenhagen: ACL, 2017. 1957–1967. [doi: 10.18653/V1/
                     D17-1209]
                 [37]  Chen HD, Huang SJ, Chiang D, Chen JJ. Improved neural machine translation with a syntax-aware encoder and decoder. In: Proc. of the
                     55th Annual Meeting of the Association for Computational Linguistics. Vancouver: ACL, 2017. 1936–1945. [doi: 10.18653/V1/P17-
                     1177]
                 [38]  Ding L, Wang LY, Liu SY. Recurrent graph encoder for syntax-aware neural machine translation. Int’l Journal of Machine Learning and
                     Cybernetics, 2023, 14(4): 1053–1062. [doi: 10.1007/s13042-022-01682-9]
                 [39]  Chen KH, Wang R, Utiyama M, Sumita E, Zhao TJ. Syntax-directed attention for neural machine translation. In: Proc. of the 32nd AAAI
                     Conf. on Artificial Intelligence. New Orleans: AAAI Press, 2018. 4792–4799. [doi: 10.1609/AAAI.V32I1.11910]
                 [40]  Duan SF, Zhao H, Zhou JR, Wang R. Syntax-aware Transformer encoder for neural machine translation. In: Proc. of the 2019 Int’l Conf.
                     on Asian Language Processing (IALP). Shanghai: IEEE, 2019. 396–401. [doi: 10.1109/IALP48816.2019.9037672]
                 [41]  Liu  Y,  Hou  YX.  Syntax-aware  complex-valued  neural  machine  translation.  In:  Proc.  of  the  32nd  Int’l  Conf.  on  Artificial  Neural
                     Networks. Heraklion: Springer, 2023. 474–485. [doi: 10.1007/978-3-031-44192-9_38]
                 [42]  Zhang MS, Li ZH, Fu GH, Zhang M. Syntax-enhanced neural machine translation with syntax-aware word representations. In: Proc. of
                     the 2019 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol.1
                     (Long and Short Papers). Minneapolis: ACL, 2019. 1151–1161. [doi: 10.18653/V1/N19-1118]
                 [43]  Wu SZ, Zhang DD, Zhang ZR, Yang N, Li M, Zhou M. Dependency-to-dependency neural machine translation. IEEE/ACM Trans. on
                     Audio, Speech, and Language Processing, 2018, 26(11): 2132–2141. [doi: 10.1109/TASLP.2018.2855968]
                 [44]  Wu SZ, Zhou M, Zhang DD. Improved neural machine translation with source syntax. In: Proc. of the 26th Int’l Joint Conf. on Artificial
                     Intelligence. Melbourne: IJCAI.org, 2017. 4179–4185. [doi: 10.24963/IJCAI.2017/584]
                 [45]  Akoury N, Krishna K, Iyyer M. Syntactically supervised Transformers for faster neural machine translation. In: Proc. of the 57th Annual
                     Meeting of the Association for Computational Linguistics. Florence: ACL, 2019. 1269–1281. [doi: 10.18653/V1/P19-1122]
                 [46]  Liu Y, Wan Y, Zhang JG, Zhao WT, Yu PS. Enriching non-autoregressive Transformer with syntactic and semantic structures for neural
                     machine translation. In: Proc. of the 16th Conf. of the European Chapter of the Association for Computational Linguistics: Main Volume.
                     ACL, 2021. 1235–1244. [doi: 10.18653/V1/2021.EACL-MAIN.105]
                 [47]  Li  JJ.  Research  on  non-autoregressive  neural  machine  translation  method  based  on  dependency  relations  [MS.  Thesis].  Kunming:
                     Kunming University of Science and Technology, 2023 (in Chinese with English abstract). [doi: 10.27200/d.cnki.gkmlu.2023.002734]
                 [48]  He KM, Zhang XY, Ren SQ, Sun J. Deep residual learning for image recognition. In: Proc of the 2016 IEEE Conf. on Computer Vision
                     and Pattern Recognition (CVPR). Las Vegas: IEEE Computer Society, 2016. 770–778. [doi: 10.1109/CVPR.2016.90]
                 [49]  Ba JL, Kiros JR, Hinton GE. Layer normalization. arXiv:1607.06450, 2016.
                 [50]  Zhang C, Li QC, Song DW. Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proc. of the
                     2019 Conf. on Empirical Methods in Natural Language Processing and the 9th Int’l Joint Conf. on Natural Language Processing (EMNLP-
                     IJCNLP). Hong Kong: ACL, 2019. 4568–4578. [doi: 10.18653/V1/D19-1464]
                 [51]  Zhang B, Zhang Y, Wang R, Li ZH, Zhang M. Syntax-aware opinion role labeling with dependency graph convolutional networks. In:
                     Proc. of the 58th Annual Meeting of the Association for Computational Linguistics. 2020. 3249–3258. [doi: 10.18653/V1/2020.ACL-
                     MAIN.297]
                 [52]  Hamming RW. Error detecting and error correcting codes. The Bell System Technical Journal, 1950, 29(2): 147–160. [doi: 10.1002/j.
                     1538-7305.1950.tb00463.x]
                 [53]  Kim  Y,  Rush  AM.  Sequence-level  knowledge  distillation.  In:  Proc.  of  the  2016  Conf.  on  Empirical  Methods  in  Natural  Language
                     Processing. Austin: ACL, 2016. 1317–1327. [doi: 10.18653/V1/D16-1139]
                 [54]  Ott M, Edunov S, Baevski A, Fan A, Gross S, Ng N, Grangier D, Auli M. FAIRSEQ: A fast, extensible toolkit for sequence modeling. In:
                     Proc. of the 2019 Conf. of the North American Chapter of the Association for Computational Linguistics (Demonstrations). Minneapolis:
                     ACL, 2019. 48–53. [doi: 10.18653/V1/N19-4009]
                 [55]  Papineni K, Roukos S, Ward T, Zhu WJ. BLEU: A method for automatic evaluation of machine translation. In: Proc. of the 40th Annual
                     Meeting of the Association for Computational Linguistics. Philadelphia: ACL, 2002. 311–318. [doi: 10.3115/1073083.1073135]
                 [56]  Kasai J, Pappas N, Peng H, Cross J, Smith NA. Deep encoder, shallow decoder: Reevaluating non-autoregressive machine translation.
                     arXiv:2006.10369, 2021.
                 [57]  Gu  JT,  Kong  X.  Fully  non-autoregressive  neural  machine  translation:  Tricks  of  the  trade.  In:  Findings  of  the  Association  for
                     Computational Linguistics: ACL-IJCNLP 2021. ACL, 2021. 120–133. [doi: 10.18653/V1/2021.FINDINGS-ACL.11]
                 [58]  Helcl J, Haddow B, Birch A. Non-autoregressive machine translation: It’s not as fast as it seems. In: Proc. of the 2022 Conf. of the North
                     American Chapter of the Association for Computational Linguistics: Human Language Technologies. Seattle: ACL, 2022. 1780–1790.
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