  In this work, we uncover a theoretical connection between two language model
interpolation techniques, count merging and Bayesian interpolation. We compare
these techniques as well as linear interpolation in three scenarios with
abundant training data per component model. Consistent with prior work, we show
that both count merging and Bayesian interpolation outperform linear
interpolation. We include the first (to our knowledge) published comparison of
count merging and Bayesian interpolation, showing that the two techniques
perform similarly. Finally, we argue that other considerations will make
Bayesian interpolation the preferred approach in most circumstances.
