Actionable Email Intent Modeling With Reparametrized RNNs


  • Chu-Cheng Lin Johns Hopkins University
  • Dongyeop Kang Carnegie Mellon University
  • Michael Gamon Microsoft Research
  • Patrick Pantel Microsoft Research



domain adaption email multitask multidomain


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.




How to Cite

Lin, C.-C., Kang, D., Gamon, M., & Pantel, P. (2018). Actionable Email Intent Modeling With Reparametrized RNNs. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).



Main Track: NLP and Knowledge Representation