The best language model performance for a task is often achieved by interpolating language
models built separately on corpora from multiple sources. While common practice is to use a
single set of fixed interpolation weights to combine models, past work has found that gains
can be had by allowing weights to vary by n-gram, when linearly interpolating word n-gram
models. In this work, we investigate whether similar ideas can be used to improve log-linear
interpolation for Model M, an exponential class-based n-gram model with state-of-the-art�…