Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations
- Resource Type
- Working Paper
- Authors
- Shimanaka, Hiroki; Kajiwara, Tomoyuki; Komachi, Mamoru
- Source
- Subject
- Computer Science - Computation and Language
- Language
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.
Comment: NAACL 2018 Student Research Workshop; 6 pages