2,121 research outputs found
Characterizing and Predicting Email Deferral Behavior
Email triage involves going through unhandled emails and deciding what to do
with them. This familiar process can become increasingly challenging as the
number of unhandled email grows. During a triage session, users commonly defer
handling emails that they cannot immediately deal with to later. These deferred
emails, are often related to tasks that are postponed until the user has more
time or the right information to deal with them. In this paper, through
qualitative interviews and a large-scale log analysis, we study when and what
enterprise email users tend to defer. We found that users are more likely to
defer emails when handling them involves replying, reading carefully, or
clicking on links and attachments. We also learned that the decision to defer
emails depends on many factors such as user's workload and the importance of
the sender. Our qualitative results suggested that deferring is very common,
and our quantitative log analysis confirms that 12% of triage sessions and 16%
of daily active users had at least one deferred email on weekdays. We also
discuss several deferral strategies such as marking emails as unread and
flagging that are reported by our interviewees, and illustrate how such
patterns can be also observed in user logs. Inspired by the characteristics of
deferred emails and contextual factors involved in deciding if an email should
be deferred, we train a classifier for predicting whether a recently triaged
email is actually deferred. Our experimental results suggests that deferral can
be classified with modest effectiveness. Overall, our work provides novel
insights about how users handle their emails and how deferral can be modeled
Learning with Weak Supervision for Email Intent Detection
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.Comment: 10 pages, 3 figure
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