Word prediction systems (such as those embedded in most current augmentative and alternative communication systems) aim to predict what a user wants to type next on the basis of corpus-extracted n-gram counts. Good performance of such a system depends crucially on the size and quality of the underlying lexicon.

In word prediction systems for augmentative and alternative communication (AAC), productive wordformation processes such as compounding pose a serious problem. We present a model that predicts German nominal compounds by splitting them into their modifier and head components, instead of trying to predict them as a whole. The model is improved further by the use of class-based modifierhead bigrams constructed using semantic classes automatically extracted from a corpus. The evaluation shows that the split compound model with class bigrams leads to an improvement in keystroke savings of more than 15% over a no split compound baseline model. We also present preliminary results obtained with a word prediction model integrating compound and simple word prediction.

Examples from chat interaction are presented to demonstrate that machine translation of written interaction shares many problems with translation of spoken interaction. The potential for common solutions to the problems is illustrated by describing operations that normalize and tag input before translation. Segmenting utterances into small translation units and processing short turns separately are also motivated using data from chat.