Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings

Junki Matsuo, Mamoru Komachi, Katsuhito Sudoh

One of the most important problems in machine translation (MT) evaluation is to evaluate the similarity between translation hypotheses with different surface forms from the reference, especially at the segment level. We propose to use word embeddings to perform word alignment for segment-level MT evaluation. We performed experiments with three types of alignment methods using word embeddings. We evaluated our proposed methods with various translation datasets. Experimental results show that our proposed methods outperform previous word embeddings-based methods.

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