  In multi-turn dialogs, natural language understanding models can introduce
obvious errors by being blind to contextual information. To incorporate dialog
history, we present a neural architecture with Speaker-Sensitive Dual Memory
Networks which encode utterances differently depending on the speaker. This
addresses the different extents of information available to the system - the
system knows only the surface form of user utterances while it has the exact
semantics of system output. We performed experiments on real user data from
Microsoft Cortana, a commercial personal assistant. The result showed a
significant performance improvement over the state-of-the-art slot tagging
models using contextual information.
