On Combining Language Models: Oracle Approach
COLORADO UNIV AT BOULDER CENTER FOR SPOKEN LANGUAGE UNDERSTANDING
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In this paper, we address the of combining several language models LMs. We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle. The oracle knows the reference word string and selects the word string with the best performance typically, word or semantic error rate from a list of word strings, where each word string has been obtained by using a different LM. Actually, the oracle acts like a dynamic combiner with hard decisions using the reference. We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further. We suggest a method that mimics the behavior of the oracle using a neural network or a decision tree. The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding the LM with the best confidence.