We introduce the RUSE 1 metric for the WMT18 metrics shared task. Sentence embeddings can capture global information that cannot be captured by local features based on character or word N-grams. Although training sentence embeddings using small-scale translation datasets with manual evaluation is difficult, sentence embeddings trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. We use a multi-layer perceptron regressor based on three types of sentence embeddings. The experimental results of the WMT16 and WMT17 datasets show that the RUSE metric achieves a state-of-the-art performance in both segment-and system-level metrics tasks with embedding features only.
RUSE REF ) is a recent effort to improve MT evaluation by training sentence embeddings on large-scale data obtained in other tasks.
RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation
WMT
Computer Science