Abstract
We propose a novel metric for machine translation evaluation based on neural networks. In the training phrase, we maximize the distance between the similarity scores of high and low-quality hypotheses. Then, the trained neural network is used to evaluate the new hypotheses in the testing phase. The proposed metric can efficiently incorporate lexical and syntactic metrics as features in the network and thus is able to capture different levels of linguistic information. Experiments on WMT-14 show state-of-the-art performance is achieved in two out of five language pairs on the system-level and one on the segment-level. Comparative results are also achieved in the remaining language pairs.
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Acknowledgements
This work is supported by National Natural Science Foundation of P. R. China under Grant No. 61379086, European Union Horizon 2020 research and innovation programme under grant agreement 645452 (QT21), and the ADAPT Centre for Digital Content Technology (www.adaptcentre.ie) at Dublin City University funded under the SFI Research Centres Programme (Grant 13/RC/2106) co-funded under the European Regional Development Fund.
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Ma, Q. et al. (2016). MaxSD: A Neural Machine Translation Evaluation Metric Optimized by Maximizing Similarity Distance. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_13
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DOI: https://doi.org/10.1007/978-3-319-50496-4_13
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