  Emails in the workplace are often intentional calls to action for its
recipients. We propose to annotate these emails for what action its recipient
will take. We argue that our approach of action-based annotation is more
scalable and theory-agnostic than traditional speech-act-based email intent
annotation, while still carrying important semantic and pragmatic information.
We show that our action-based annotation scheme achieves good inter-annotator
agreement. We also show that we can leverage threaded messages from other
domains, which exhibit comparable intents in their conversation, with domain
adaptive RAINBOW (Recurrently AttentIve Neural Bag-Of-Words). On a collection
of datasets consisting of IRC, Reddit, and email, our reparametrized RNNs
outperform common multitask/multidomain approaches on several speech act
related tasks. We also experiment with a minimally supervised scenario of email
recipient action classification, and find the reparametrized RNNs learn a
useful representation.
