An Empirical Study on Language Model Adaptation Using a Metric of Domain Similarity

This paper presents an empirical study on four techniques of language model adaptation, including a maximum a posteriori (MAP) method and three discriminative training models, in the application of Japanese Kana-Kanji conversion. We compare the performance of these methods from various angles by adapting the baseline model to four adaptation domains. In particular, we at-tempt to interpret the results given in terms of the character error rate (CER) by correlating them with the characteristics of the adaptation domain measured us-ing the information-theoretic notion of cross entropy. We show that such a met-ric correlates well with the CER performance of the adaptation methods, and also show that the discriminative methods are not only superior to a MAP-based method in terms of achieving larger CER reduction, but are also more ro-bust against the similarity of background and adaptation domains.