Unsupervised Embedding-based Metric for MT Evaluation with Improved Human Correlation

AbstractIn this paper, we describe our submission to the WMT22 metrics shared task. Our metric focuses on computing contextual and syntactic equivalences along with lexical, morphological, and semantic similarity. The intent is to capture the fluency and context of the MT outputs along with their adequacy. Fluency is captured using syntactic similarity and context is captured using sentence similarity leveraging sentence embeddings. The final sentence translation score is the weighted combination of three similarity scores: a) Syntactic Similarity b) Lexical, Morphological and Semantic Similarity, and c) Contextual Similarity. This paper outlines two improved versions of MEE i.e., MEE2 and MEE4. Additionally, we report our experiments on language pairs of en-de, en-ru and zh-en from WMT17-19 testset and further depict the correlation with human assessments.