  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.

Computation and Language