Ilya Oparin (Apple, USA): Connecting and Comparing Language Model Interpolation Techniques

IlyaIlya Oparin is leading Language Modeling team that contributes to improving Siri at Apple. He did his Ph.D. on language modeling of inflectional languages at University of West Bohemia in collaboration with Speech@FIT group at Brno University of Technology. Before joining Apple in 2014, Ilya did 3 years of post-doc in Spoken Language Processing group at LIMSI. Ilya’s research interests cover any topics related to language modeling for automatic speech recognition and more broadly for natural language processing.

Connecting and Comparing Language Model Interpolation Techniques

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.

His talk takes place on Thursday, December 19, 2019 at 13:00 in “little theater” R211 (next to Kachnicka student club in “Stary Pivovar”).