Abstract
Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing efforts. Training on APE models has made a great progress since 2015; however, whether APE models are really performing well on domain samples remains as an open question, and achieving this is still a hard task. This paper provides a mobile domain APE corpus with 50.1 TER/37.4 BLEU for the En-Zh language pair. This corpus is much more practical than that provided in WMT 2021 APE tasks (18.05 TER/71.07 BLEU for En-De, 22.73 TER/69.2 BLEU for En-Zh) [1]. To obtain a more comprehensive investigation on the presented corpus, this paper provides two mainstream models as the Cookbook baselines: (1) Autoregressive Translation APE model (AR-APE) based on HW-TSC APE 2020 [2], which is the SOTA model of WMT 2020 APE tasks. (2) Non-Autoregressive Translation APE model (NAR-APE) based on the well-known Levenshtein Transformer [3]. Experiments show that both the mainstream models of AR and NAR can effectively improve the effect of APE. The corpus has been released in the CCMT 2022 APE evaluation task and the baseline models will be open-sourced.
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References
Akhbardeh, F., et al.: Findings of the 2021 conference on machine translation (WMT21). In: Proceedings of the Sixth Conference on Machine Translation, pp. 1–88. Association for Computational Linguistics, November 2021 (Online)
Yang, H., et al.: HW-TSC’s participation at WMT 2020 automatic post editing shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp. 797–802. Association for Computational Linguistics, November 2020 (Online)
Gu, J., Wang, C., Zhao, J.: Levenshtein transformer. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Bojar, O., et al.: Findings of the 2015 workshop on statistical machine translation. In: Proceedings of the Tenth Workshop on Statistical Machine Translation, Lisbon, Portugal, pp. 1–46. Association for Computational Linguistics, September 2015
Junczys-Dowmunt, M.: Are we experiencing the golden age of automatic post-editing? In: Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing, Boston, MA, pp. 144–206. Association for Machine Translation in the Americas, March 2018
Akhbardeh, F., et al.: Findings of the 2021 conference on machine translation (WMT21). In: Proceedings of the Sixth Conference on Machine Translation, pp. 1–88 (2021)
Chatterjee, R., Federmann, C., Negri, M., Turchi, M.: Findings of the WMT 2020 shared task on automatic post-editing. In: Barrault, L., et al. (eds.) Proceedings of the Fifth Conference on Machine Translation, WMT@EMNLP 2020, 19–20 November 2020, pp. 646–659. Association for Computational Linguistics (2020, online)
Gu, J., Bradbury, J., Xiong, C., Li, V.O.K., Socher, R.: Non-autoregressive neural machine translation. In: International Conference on Learning Representations (2018)
Snover, M., Dorr, B.J., Schwartz, R., Micciulla, L.: A study of translation edit rate with targeted human annotation (2006)
Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, pp. 311–318. Association for Computational Linguistics, July 2002
Chatterjee, R., Freitag, M., Negri, M., Turchi, M.: Findings of the WMT 2020 shared task on automatic post-editing. In: Proceedings of the Fifth Conference on Machine Translation, pp. 646–659. Association for Computational Linguistics, November 2020 (Online)
Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA, 4–9 December 2017, pp. 5998–6008 (2017)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019)
Lopes, A.V., Farajian, M.A., Correia, G.M., Trénous, J., Martins, A.F.: Unbabel’s submission to the WMT2019 APE shared task: Bert-based encoder-decoder for automatic post-editing. CoRR, abs/1905.13068 (2019)
Gu, J., Wang, C., Zhao, J.: Levenshtein transformer. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, Vancouver, BC, Canada, 8–14 December 2019, pp. 11179–11189 (2019)
Chollampatt, S., Susanto, R.H., Tan, L., Szymanska, E.: Can automatic post-editing improve NMT? arXiv preprint arXiv:2009.14395 (2020)
Wang, M., et al.: HW-TSC’s participation at WMT 2020 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp. 1056–1061. Association for Computational Linguistics, November 2020 (Online)
Yang, H., et al. Hw-TSC’s submissions to the WMT21 biomedical translation task. In: Proceedings of the Sixth Conference on Machine Translation, pp. 879–884 (2021)
Ng, N., Yee, K., Baevski, A., Ott, M., Auli, M., Edunov, S.: Facebook fair’s WMT19 news translation task submission. In: Bojar, O., et al. (eds.) Proceedings of the Fourth Conference on Machine Translation, WMT 2019, Florence, Italy, 1–2 August 2019 - Volume 2: Shared Task Papers, Day 1, pp. 314–319. Association for Computational Linguistics (2019)
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Tao, S. et al. (2022). PEACook: Post-editing Advancement Cookbook. In: Xiao, T., Pino, J. (eds) Machine Translation. CCMT 2022. Communications in Computer and Information Science, vol 1671. Springer, Singapore. https://doi.org/10.1007/978-981-19-7960-6_1
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