colloquial Finnish has been translated to standard Finnish using both rule-based normalization and statistical machine translation [@Listenmaa:2015].

Two constraints makes our task different from text normalization: A word needs to be classified in the same way regardless of the context, and a word cannot be mapped to a word sequence. Our last clustering method, *Rules*, is based on a set of rules that describe the usual reductions and alternations in colloquial words. We iterate over a standard Finnish vocabulary and compare the standard Finnish word with every word in a colloquial Finnish vocabulary. If the colloquial word appears to be a reduced pronunciation of the standard word, these words are merged into a class. Because all the words can appear in at most one class, multiple standard words can be merged into one class, but this is rare. Thus, there will be only a handful of words in each class. Larger classes can be created by merging the classes produced by this algorithm using some other clustering technique.

Subword Language Models
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Subword modeling is another effective technique to reduce vocabulary size. We use the Morfessor method [@Creutz:2002; @Creutz:2007:1], which has been successfully applied in speech recognition of many agglutinative 