  Intent detection is a key part of any Natural Language Understanding (NLU)
system of a conversational assistant. Detecting the correct intent is essential
yet difficult for email conversations where multiple directives and intents are
present. In such settings, conversation context can become a key disambiguating
factor for detecting the user's request from the assistant. One prominent way
of incorporating context is modeling past conversation history like
task-oriented dialogue models. However, the nature of email conversations (long
form) restricts direct usage of the latest advances in task-oriented dialogue
models. So in this paper, we provide an effective transfer learning framework
(EMToD) that allows the latest development in dialogue models to be adapted for
long-form conversations. We show that the proposed EMToD framework improves
intent detection performance over pre-trained language models by 45% and over
pre-trained dialogue models by 30% for task-oriented email conversations.
Additionally, the modular nature of the proposed framework allows plug-and-play
for any future developments in both pre-trained language and task-oriented
dialogue models.
