Adapting language models for frequent fixed phrases by emphasizing n-gram subsets
Tomoyosi Akiba, Katunobu Itou, Atsushi Fujii · 2003
In support of speech-driven question answering, we propose a method to construct N-gram language models for recognizing spoken questions with high accuracy. Question-answering sys-tems receive queries that often consist of two parts: one conveys the query topic and the other is a fixed phrase used in query sentences. A language model constructed by using a target col-lection of QA, for example, newspaper articles, can model the former part, but cannot model the latter part appropriately. We tackle this problem as task adaptation from language models ob-tained from background corpora (e.g., newspaper articles) to the fixed phrases, and propose a method that does not use the task-specific corpus, which is often difficult to obtain, but instead uses only manually listed fixed phrases. The method empha-sizes a subset of N-grams obtained from a background corpus that corresponds to fixed phrases specified by the list. Theoret-ically, this method can be regarded as maximizing a posteriori probability (MAP) estimation using the subset of the N-grams as a posteriori distribution. Some experiments show the effec-tiveness of our method. 1.