RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation
Hiroki Shimanaka, Tomoyuki Kajiwara, Mamoru Komachi · 2018
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.