Morphology Generation for Statistical Machine Translation

TL;DR

This paper proposes a morphology injection method that enriches training data with morphological forms to improve statistical machine translation quality for morphologically rich languages like Hindi and Marathi.

Contribution

It introduces a simple, effective data enrichment technique that alleviates data sparsity issues in SMT for morphologically rich languages, enhancing translation performance.

Findings

Improved translation quality in Hindi and Marathi.

Morphology injection reduces data sparsity effects.

Enhances both automatic and subjective evaluation scores.

Abstract

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.