Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations
This addresses the challenge of automatic evaluation for machine translation systems, offering a more accurate and data-efficient approach compared to traditional n-gram methods.
The paper tackled the problem of evaluating machine translation quality by proposing a method based on universal sentence representations, achieving state-of-the-art performance on the WMT-2016 dataset using only sentence representation features.
Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams. This paper examines the usefulness of universal sentence representations for evaluating the quality of machine translation. Although it is difficult to train sentence representations using small-scale translation datasets with manual evaluation, sentence representations trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. Experimental results of the WMT-2016 dataset show that the proposed method achieves state-of-the-art performance with sentence representation features only.