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

ACL ID I05-1083
Title An Empirical Study on Language Model Adaptation Using a Metric of Domain Similarity
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors

This paper presents an empirical study on four techniques of lan- guage 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...