Abstract
This paper describes experiments carried out utilizing a variety of machine-learning methods (the k-nearest neighborhood, decision list, maximum entropy, and support vector machine), and using six machine-translation (MT) systems available on the market for translating tense, aspect, and modality. We found that all these, including the simple string-matching-based k-nearest neighborhood used in a previous study, obtained higher accuracy rates than the MT systems currently available on the market. We also found that the support vector machine obtained the best accuracy rates (98.8%) of these methods. Finally, we analyzed errors against the machine-learning methods and commercially available MT systems and obtained error patterns that should be useful for making future improvements.
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Notes
The gold standard data were prepared by using system outputs and the gold standard was then biased using these system outputs. However, the bias was small because we used a tag in the corpus and also tense/aspect/modality expressions generated by three translators.
There might be a small number of cases where the tense/aspect/modality categories of a MT system are judged to be incorrect when a tense/aspect/modality selection inside the MT engine is correct but the generation module produces a wrong output string.
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Murata, M., Ma, Q., Uchimoto, K. et al. Japanese-to-English translations of tense, aspect, and modality using machine-learning methods and comparison with machine-translation systems on market. Lang Resources & Evaluation 40, 233–242 (2006). https://doi.org/10.1007/s10579-007-9022-z
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DOI: https://doi.org/10.1007/s10579-007-9022-z