Application and Analysis of Sentence Similarity Based Machine Translation Evaluation

Abstract

To help developing a localization oriented example based machine translation (EBMT) system, an automatic machine translation evaluation method is implemented which adopts edit similarity, cosine correlation and Dice coefficient as criteria. Experiment shows that the evaluation method distinguishes well between translations of different intelligibility and fluency. The similarity between Dice coefficient and cosine are analyzed mathematically and observed in the experiments. To verify theconsistency between automatic and human evaluation methods, six machine translation systems are scored using both human and automatic methods. The evaluation results are compared which show consistency between different evaluation methods. Statistical analysis is made to validate the experimental results. Correlation coefficient and significance tests at 99%level are made to ensure the reliability of the results. Linear regression equations are built to map the automatic scoring results to human scorings. The regression equation is utilized to predict human scoring of machine translation systems. The prediction result is promising. Experimental results show that the proposed MT evaluation method is applicable to general MT systems and EBMT as well.