Actionable Email Intent Modeling with Reparametrized RNNs

TL;DR

This paper introduces an action-based annotation scheme for workplace emails, leveraging reparametrized RNNs to improve intent detection across domains with minimal supervision.

Contribution

It proposes a scalable, theory-agnostic annotation method and a domain adaptive RAINBOW model that outperforms traditional approaches on multiple datasets.

Findings

High inter-annotator agreement achieved.

Reparametrized RNNs outperform standard models on speech act tasks.

Effective in minimally supervised email recipient action classification.

Abstract

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.