"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

Social agents for learning in virtual environments

Several serious games have been proposed to practice communication strategies in formal contexts. Intelligent virtual agents (IVA) can be used to show the player the effects of a conversational move. In this paper we discuss the key role of using social context for the virtual agents in these serious games. Social practices are exploited to bundle social interactions into standard packages and as a basis to model the deliberation processes of IVAs. We describe a social practice oriented IVA architecture used in the implementation of a serious game for the practicing of communication in medical interviews

The Optique Project: Towards OBDA Systems for Industry (Short Paper)

fluid Operations AG

fluid Operations AG,

Accessing the relevant data in Big Data scenarios is increasingly difficult both for end-user and IT-experts, due to the volume, variety, and velocity dimensions of Big Data.This brings a hight cost overhead in data access for large enterprises. For instance, in the oil and gas industry, IT-experts spend 30–70 % of their time gathering and assessing the quality of data [1]. The Optique projec