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Optimizing Automatic Evaluation of Machine Translation with the ListMLE Approach

机译:使用ListMLE方法优化机器翻译的自动评估

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摘要

Automatic evaluation of machine translation is critical for the evaluation and development of machine translation systems. In this study, we propose a new model for automatic evaluation of machine translation. The proposed model combines standard n-gram precision features and sentence semantic mapping features with neural features, including neural language model probabilities and the embedding distances between translation outputs and their reference translations. We optimize the model with a representative list-wise learning to rank approach, ListMLE, in terms of human ranking assessments. The experimental results on WMT'2015 Metrics task indicated that the proposed approach yields significantly better correlations with human assessments than several state-of-the-art baseline approaches. In particular, the results confirmed that the proposed list-wise learning to rank approach is useful and powerful for optimizing automatic evaluation metrics in terms of human ranking assessments. Deep analysis also demonstrated that optimizing automatic metrics with the ListMLE approach is a reasonable method and adding the neural features can gain considerable improvements compared with the traditional features.
机译:机器翻译的自动评估对于评估和开发机器翻译系统至关重要。在这项研究中,我们提出了一种自动评估机器翻译的新模型。所提出的模型将标准的n-gram精度特征和句子语义映射特征与神经特征相结合,包括神经语言模型的概率以及翻译输出与其参考翻译之间的嵌入距离。我们通过对人员进行排名评估来对具有代表性的基于列表的学习进行排名模型ListMLE进行优化。关于WMT'2015 Metrics任务的实验结果表明,与几种最新的基准方法相比,该方法与人类评估的相关性明显更好。尤其是,结果证实了所提出的基于列表的学习排序方法对于根据人类排名评估来优化自动评估指标是有用且强大的。深入分析还表明,使用ListMLE方法优化自动度量是一种合理的方法,并且与传统功能相比,添加神经功能可以获得很大的改进。

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