Word Embedding-Based Automatic MT Evaluation Metric using Word Position Information

We propose a new automatic evaluation metric for machine translation. Our proposed metric is obtained by adjusting the Earth Mover{'}s Distance (EMD) to the evaluation task. The EMD measure is used to obtain the distance between two probability distributions consisting of some signatures having a feature and a weight. We use word embeddings, sentence-level tf-idf, and cosine similarity between two word embeddings, respectively, as the features, weight, and the distance between two features. Results show that our proposed metric can evaluate machine translation based on word meaning. Moreover, for distance, cosine similarity and word position information are used to address word-order differences. We designate this metric as Word Embedding-Based automatic MT evaluation using Word Position Information (WE{\_}WPI). A meta-evaluation using WMT16 metrics shared task set indicates that our WE{\_}WPI achieves the highest correlation with human judgment among several representative metrics.