This paper presents comparative experimen- tal results on four techniques of language model adaptation, including a maximum a posteriori (MAP) method and three dis- criminative training methods, the boosting algorithm, the average perceptron and the minimum sample risk method, on the task of Japanese Kana-Kanji conversion. We evalu- ate these techniques beyond simply using the character error rate (CER): the CER re- sults are interpreted using a metric of do- main similarity between background and adaptation domains, and are further evalu- ated by correlating them with a novel metric for measuring the side effects of adapted models. Using these metrics, we show that the discriminative methods are superior to a MAP-based method not only in terms of achieving larger CER reduction, but also o...

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