  Intelligent assistants like Cortana, Siri, Alexa, and Google Assistant are
trained to parse information when the conversation is synchronous and short;
however, for email-based conversational agents, the communication is
asynchronous, and often contains information irrelevant to the assistant. This
makes it harder for the system to accurately detect intents, extract entities
relevant to those intents and thereby perform the desired action. We present a
neural model for scoping relevant information for the agent from a large query.
We show that when used as a preprocessing step, the model improves performance
of both intent detection and entity extraction tasks. We demonstrate the
model's impact on Scheduler (Cortana is the persona of the agent, while
Scheduler is the name of the service. We use them interchangeably in the
context of this paper.) - a virtual conversational meeting scheduling assistant
that interacts asynchronously with users through email. The model helps the
entity extraction and intent detection tasks requisite by Scheduler achieve an
average gain of 35% in precision without any drop in recall. Additionally, we
demonstrate that the same approach can be used for component level analysis in
large documents, such as signature block identification.
