Word-Level Language Identification and Predicting Codeswitching Points in Swahili-English Language Data

Codeswitching is a very common behavior among Swahili speakers, but of the little computational work done on Swahili, none has focused on codeswitching. This paper addresses two tasks relating to Swahili-English codeswitching: word-level language identification and prediction of codeswitch points. Our two-step model achieves high accuracy at labeling the language of words using a simple feature set combined with label probabilities on the adjacent words. This system is used to label a large Swahili-English internet corpus, which is in turn used to train a model for predicting codeswitch points.