  Statistical language models (LM) play a key role in Automatic Speech
Recognition (ASR) systems used by conversational agents. These ASR systems
should provide a high accuracy under a variety of speaking styles, domains,
vocabulary and argots. In this paper, we present a DNN-based method to adapt
the LM to each user-agent interaction based on generalized contextual
information, by predicting an optimal, context-dependent set of LM
interpolation weights. We show that this framework for contextual adaptation
provides accuracy improvements under different possible mixture LM partitions
that are relevant for both (1) Goal-oriented conversational agents where it's
natural to partition the data by the requested application and for (2) Non-goal
oriented conversational agents where the data can be partitioned using topic
labels that come from predictions of a topic classifier. We obtain a relative
WER improvement of 3% with a 1-pass decoding strategy and 6% in a 2-pass
decoding framework, over an unadapted model. We also show up to a 15% relative
improvement in recognizing named entities which is of significant value for
conversational ASR systems.
