Lexical-based metrics such as BLEU, NIST, and WER have been widely used in machine translation (MT) evaluation. However, these metrics badly represent semantic relationships and impose strict identity matching, leading to moderate correlation with human judgments. In this paper, we propose a novel MT automatic evaluation metric Semantic Travel Distance (STD) based on word embeddings. STD incorporates both semantic and lexical features (word embeddings and n -gram and word order) into one metric. It measures the semantic distance between the hypothesis and reference by calculating the minimum cumulative cost that the embedded n -grams of the hypothesis need to “travel” to reach the embedded n -grams of the reference. Experiment results show that STD has a better and more robust performance than a range of state-of-the-art metrics for both the segment-level and system-level evaluation.
