Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings
Table 1: Kendall’s τ correlations of automatic evaluation metrics and official human judgements for the WMT15 dataset. (Fr: French, Fi: Finnish, De: German, Cs: Czech, Ru: Russian, En: English)
['Evaluation Metrics', 'Fr-En', 'Fi-En', 'De-En', 'Cs-En', 'Ru-En', 'Average']
MAS shows the best accuracy among all the proposed metrics for all language pairs. Its accuracy is better than that of DREEM for all language pairs except for Czech–English. This result shows that removal of noisy word embeddings by either using a threshold or 1: n alignment is important for European–English datasets.