18,723 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
A Generative Model of Group Conversation
Conversations with non-player characters (NPCs) in games are typically
confined to dialogue between a human player and a virtual agent, where the
conversation is initiated and controlled by the player. To create richer, more
believable environments for players, we need conversational behavior to reflect
initiative on the part of the NPCs, including conversations that include
multiple NPCs who interact with one another as well as the player. We describe
a generative computational model of group conversation between agents, an
abstract simulation of discussion in a small group setting. We define
conversational interactions in terms of rules for turn taking and interruption,
as well as belief change, sentiment change, and emotional response, all of
which are dependent on agent personality, context, and relationships. We
evaluate our model using a parameterized expressive range analysis, observing
correlations between simulation parameters and features of the resulting
conversations. This analysis confirms, for example, that character
personalities will predict how often they speak, and that heterogeneous groups
of characters will generate more belief change.Comment: Accepted submission for the Workshop on Non-Player Characters and
Social Believability in Games at FDG 201
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
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