WMDO: Fluency-based Word Mover's Distance for Machine Translation Evaluation
We propose WMDO, a metric based on distance between distributions in the semantic vector space. Matching in the semantic space has been investigated for translation evaluation, but the constraints of a translation{'}s word order have not been fully explored. Building on the Word Mover{'}s Distance metric and various word embeddings, we introduce a fragmentation penalty to account for fluency of a translation. This word order extension is shown to perform better than standard WMD, with promising results against other types of metrics.
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