Morphology Generation for Statistical Machine Translation

When translating into morphologically rich languages, Statistical MT approaches face the problem of data sparsity. The severity of the sparseness problem will be high when the corpus size of morphologically richer language is less. Even though we can use factored models to correctly generate morphol...

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Bibliographic Details
Published inarXiv.org
Main Authors Sreelekha, S, Bhattacharyya, Pushpak
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 10.11.2017
Subjects
Adequacy
Languages
Machine translation
Morphology
Performance evaluation
Translating
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Summary:When translating into morphologically rich languages, Statistical MT approaches face the problem of data sparsity. The severity of the sparseness problem will be high when the corpus size of morphologically richer language is less. Even though we can use factored models to correctly generate morphological forms of words, the problem of data sparseness limits their performance. In this paper, we describe a simple and effective solution which is based on enriching the input corpora with various morphological forms of words. We use this method with the phrase-based and factor-based experiments on two morphologically rich languages: Hindi and Marathi when translating from English. We evaluate the performance of our experiments both in terms automatic evaluation and subjective evaluation such as adequacy and fluency. We observe that the morphology injection method helps in improving the quality of translation. We further analyze that the morph injection method helps in handling the data sparseness problem to a great level.
ISSN:2331-8422