Translation of Tense, Aspect, and Modality

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Title: Translation of Tense, Aspect, and Modality

Research Question: Can machine learning methods improve the translation of tense, aspect, and modality in Japanese-English sentences?

Methodology: The researchers used a variety of machine learning methods, including the k-nearest neighborhood method, to analyze a corpus of Japanese-English sentences. They used all the morphemes from each sentence, as well as the strings at the ends of the sentences, as information.

Results: The support-vector machine method was found to be the most precise of all the methods tested. Adding morpheme information from each sentence was significantly effective in improving the precision of the translations.

Implications: This study suggests that machine learning methods can improve the translation of tense, aspect, and modality in Japanese-English sentences. The support-vector machine method was found to be the most precise, indicating that it could be a useful tool for improving the accuracy of machine translation.

Link to Article: https://arxiv.org/abs/0112003v1 Authors: arXiv ID: 0112003v1

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