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The under-resourced Kikamba language has few language technology tools since the more efficient and popular data driven approaches for developing them suffer from data sparseness due to lack of digitized corpora. To address this challenge, we have developed a computational grammar for the Kikamba language within the multilingual Grammatical Framework (GF) toolkit. GF uses the Interlingua rule-based translation approach. To develop the grammar, we used the morphology driven strategy. Therefore, we first developed regular expressions for morphology inflection and thereafter developed the syntax rules. Evaluation of the grammar was done using one hundred sentences in both English and Kikamba languages. The results were an encouraging four n-gram BLEU score of 83.05% and the Position independent error rate (PER) of 10.96%. Finally, we have made a contribution to the language technology resources for Kikamba including multilingual machine translation, a morphology analyzer, a computational grammar which provides a platform for development of multilingual applications and the ability to generate a variety of bilingual corpora for Kikamba for all languages currently defined in GF, making it easier to experiment with data driven approaches.

This paper elucidates the InterlinguaPlus design and its application in bi-directional text translations between Ekegusii and Kiswahili languages unlike the traditional translation pairs, one-by-one. Therefore, any of the languages can be the source or target language. The first section is an overview of the project, which is followed by a brief review of Machine Translation. The next section discusses the implementation of the system using Carabao's open machine translation framework and the results obtained. So far, the translation results have been plausible particularly for the resource-scarce local languages and clearly affirm morphological similarities inherent in Bantu languages.

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The knowledge-driven economy uses technology, thereby increasing the demand for language tools and resources to acquire and distribute the knowledge. Such tools and resources are scarce for the under resourced, spoken Bantu languages. This paper develops a computational grammar for the Ekegusii language in the Grammatical Framework (GF) to bridge the gap. The grammar development uses a bottom-up and modular-driven approach. A machine translation experiment was set up to evaluate the grammar resulting in BLEU and PER of 55.95% and 19.49%, respectively. This work contributes by providing computational grammar for an under-resourced language, thus providing a platform for analysis and synthesis, plus a machine translation within the GF ecosystem.

The knowledge-driven economy uses technology, thereby increasing the demand for language tools and resources to acquire and distribute the knowledge. Such tools and resources are scarce for the under resourced, spoken Bantu languages. This paper develops a computational grammar for the Ekegusii language in the Grammatical Framework (GF) to bridge the gap. The grammar development uses a bottom-up and modular-driven approach. A machine translation experiment was set up to evaluate the grammar resulting in BLEU and PER of 55.95% and 19.49%, respectively. This work contributes by providing computational grammar for an under-resourced language, thus providing a platform for analysis and synthesis, plus a machine translation within the GF ecosystem.

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