Learning with Weak Supervision for Email Intent Detection
- URL: http://arxiv.org/abs/2005.13084v1
- Date: Tue, 26 May 2020 23:41:05 GMT
- Title: Learning with Weak Supervision for Email Intent Detection
- Authors: Kai Shu, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah,
Milad Shokouhi, Susan Dumais
- Abstract summary: We propose to leverage user actions as a source of weak supervision to detect intents in emails.
We develop an end-to-end robust deep neural network model for email intent identification.
- Score: 56.71599262462638
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Email remains one of the most frequently used means of online communication.
People spend a significant amount of time every day on emails to exchange
information, manage tasks and schedule events. Previous work has studied
different ways for improving email productivity by prioritizing emails,
suggesting automatic replies or identifying intents to recommend appropriate
actions. The problem has been mostly posed as a supervised learning problem
where models of different complexities were proposed to classify an email
message into a predefined taxonomy of intents or classes. The need for labeled
data has always been one of the largest bottlenecks in training supervised
models. This is especially the case for many real-world tasks, such as email
intent classification, where large scale annotated examples are either hard to
acquire or unavailable due to privacy or data access constraints. Email users
often take actions in response to intents expressed in an email (e.g., setting
up a meeting in response to an email with a scheduling request). Such actions
can be inferred from user interaction logs. In this paper, we propose to
leverage user actions as a source of weak supervision, in addition to a limited
set of annotated examples, to detect intents in emails. We develop an
end-to-end robust deep neural network model for email intent identification
that leverages both clean annotated data and noisy weak supervision along with
a self-paced learning mechanism. Extensive experiments on three different
intent detection tasks show that our approach can effectively leverage the
weakly supervised data to improve intent detection in emails.
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