697 research outputs found
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
Given the increasing popularity of customer service dialogue on Twitter,
analysis of conversation data is essential to understand trends in customer and
agent behavior for the purpose of automating customer service interactions. In
this work, we develop a novel taxonomy of fine-grained "dialogue acts"
frequently observed in customer service, showcasing acts that are more suited
to the domain than the more generic existing taxonomies. Using a sequential
SVM-HMM model, we model conversation flow, predicting the dialogue act of a
given turn in real-time. We characterize differences between customer and agent
behavior in Twitter customer service conversations, and investigate the effect
of testing our system on different customer service industries. Finally, we use
a data-driven approach to predict important conversation outcomes: customer
satisfaction, customer frustration, and overall problem resolution. We show
that the type and location of certain dialogue acts in a conversation have a
significant effect on the probability of desirable and undesirable outcomes,
and present actionable rules based on our findings. The patterns and rules we
derive can be used as guidelines for outcome-driven automated customer service
platforms.Comment: 13 pages, 6 figures, IUI 201
Hi, how can I help you?: Automating enterprise IT support help desks
Question answering is one of the primary challenges of natural language
understanding. In realizing such a system, providing complex long answers to
questions is a challenging task as opposed to factoid answering as the former
needs context disambiguation. The different methods explored in the literature
can be broadly classified into three categories namely: 1) classification
based, 2) knowledge graph based and 3) retrieval based. Individually, none of
them address the need of an enterprise wide assistance system for an IT support
and maintenance domain. In this domain the variance of answers is large ranging
from factoid to structured operating procedures; the knowledge is present
across heterogeneous data sources like application specific documentation,
ticket management systems and any single technique for a general purpose
assistance is unable to scale for such a landscape. To address this, we have
built a cognitive platform with capabilities adopted for this domain. Further,
we have built a general purpose question answering system leveraging the
platform that can be instantiated for multiple products, technologies in the
support domain. The system uses a novel hybrid answering model that
orchestrates across a deep learning classifier, a knowledge graph based context
disambiguation module and a sophisticated bag-of-words search system. This
orchestration performs context switching for a provided question and also does
a smooth hand-off of the question to a human expert if none of the automated
techniques can provide a confident answer. This system has been deployed across
675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201
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