Abstract
This paper introduces the evaluation procedure, evaluation data, participants and evaluation results of 2019 (15th) China Conference on Machine Translation (CCMT 2019) evaluation campaign. Compared with the last evaluation campaign (CWMT 2018), CCMT 2019 MT evaluation is characterized as follows: a new speech translation task is fulfilled; the translation quality estimation task is augmented with a word level track in addition to the sentence level track. Meanwhile, CCMT 2019 receives increases in the number of participants and systems submitted. This paper presents the anonymous evaluation results of all tasks, with a brief summarization of the techniques applied in this evaluation campaign.
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Notes
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One team can’t be contacted after registration, which is not counted as in the 30 teams.
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Available only in Chinese via https://ccmt2019.jxnu.edu.cn/page/main1923/CCMT2019_Evaluation_report.zip.
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References
Huang, S., Jiaermuhamaiti, Y., Zhao, H., Liu, Q.: The 14th China workshop on machine translation (CWMT 2018) evaluation report. In: The 14th China Workshop on Machine Translation (CWMT 2018), Wuyishan, China, 25–26 October 2018
Wang, K., Jiang, W., Yang, H., Xiang, L., Zhao, H.: The 11th China workshop on machine translation (CWMT 2015) evaluation report. In: The 11th China Workshop on Machine Translation (CWMT 2015), Hefei, China, 24–25 September 2015
Zhao, H., Xie, J., Lv, Y., Yu, H., Zhang, H., Liu, Q.: The 9th China workshop on machine translation (CWMT 2013) evaluation report. In: The 9th China Workshop on Machine Translation (CWMT 2013), Kunming, China, 31 October–1 November 2013
Barrault, L., et al.: Findings of the 2019 conference on machine translation (WMT19). In: Proceedings of ACL 2019 Fourth Conference on Machine Translation (WMT19), Florence, August 2019
Vaswani, A., et al.: Attention is all you need. In: Proceedings of Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Sennrich, R., Haddow, B., Birch, A.: Improving neural machine translation models with monolingual data. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 86–96 (2016)
Ott, M., et al.: FAIRSEQ: a fast, extensible toolkit for sequence modeling. In: Proceedings of NAACL HLT 2019 (2019)
Acknowledgments
Our special gratitude goes to partners who providing the data resources (with no special order): Alibaba (China) Co., Ltd., Baidu inc., Beijing Lingosail Tech Co., Ltd., DianTongShuJu Co., Ltd., Northeastern University, Harbin Institute of Technology, Nanjing University, Inner Mongolia University, Qinghai Normal University, Northwest Minzu University, Tibet University, Xiamen University, Institute of Intelligent Machines. CAS, Institute of Computing Technology (CAS), The Xinjiang Technical Institute of Physics & Chemistry (CAS), and Institute of Automation (CAS). We would also express gratitude to the Technical Committee of Machine Translation of CIPSC and all the participating teams for their various supports and contributions to CCMT 2019 evaluation campaign.
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Yang, M. et al. (2019). CCMT 2019 Machine Translation Evaluation Report. In: Huang, S., Knight, K. (eds) Machine Translation. CCMT 2019. Communications in Computer and Information Science, vol 1104. Springer, Singapore. https://doi.org/10.1007/978-981-15-1721-1_11
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DOI: https://doi.org/10.1007/978-981-15-1721-1_11
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