A Knowledge-Light Approach to Luo Machine Translation and Part-of-Speech Tagging

Abstract

This paper describes the collection and exploitation of a small trilingual corpus English - Swahili - Luo (Dholuo). Taking advantage of existing morphosyntactic annotation tools for English and Swahili and the unsupervised induction of Luo morphological segmentation patterns, we are able to perform fairly accurate word alignment on factored data. This not only enables the development of a workable bidirectional statistical machine translation system English - Luo, but also allows us to part-of-speech tag the Luo data using the projection of annotation technique. The experiments described in this paper demonstrate how this knowledge-light and language-independent approach to machine translation and part-of-speech tagging can result in the fast development of language technology components for a resource-scarce language.