We are modeling roles of individual messages and participants in Q&A discussion forums. In this paper, we present a mixed network model that represents message exchanges and message influences within a discussion thread. We first model individual message roles and thread-level user roles using discussion content features. We then combine the resulting message roles and the user roles to generat...

We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoder-decoder framework: it formalizes the generation of response as a decoding process based on the latent representation of the input text, while both encoding and decoding are realized with recurrent neural networks (RNN). The NRM is trained with a large ...

In this article we argue for the central role of conversations in knowledge sharing. Given the importance attributed to conversations in the literature, but also the many conversational routines that often prevent knowledge sharing, an integrative approach for the management of knowledge-intensive conversations is needed. We review the literature on conversations in organizations in order to de...

This paper provides an overview of DeepIntell’s system participated in the Short Text Conversation (STC2) task of Chinese at NTCIR-13. Previous STC of NTCIR-12 is a conversation task which can be defined as an IR problem, i.e.,retrieval based a repository of post-comment pairs. STC2 of NTCIR-13 provided a transparent platform to compare the generation-based method and IR method via comprehensiv...

This paper presents a novel framework for modeling embodied conversational agent for crisis communication focusing on the H5N1 pandemic crisis. Our system aims to cope with the most challenging issue on the maintenance of an engaging while convincing conversation. What primarily distinguishes our system from other conversational agent systems is that the human-computer conversation takes place ...

In complex conversation tasks, people react to their interlocutor’s state, such as uncertainty and engagement to improve conversation effectiveness [2]. If a conversational system reacts to a user’s state, would that lead to a better conversation experience? To test this hypothesis, we designed and implemented a dialog system that tracks and reacts to a user’s state, such as engagement, in real...

In MultiAgent Based Simulation (MABS), the observation of simulation results is a complex task: interactions between agents produce a large mass of results, which is particularly complex to analyze. We focus on agents' interactions by message exchanges, from the point of view of human observers. In order to facilitate the observation and the analysis of interactions, we propose an upper level o...

This paper deals with the automatic analysis of conversations between a customer and an agent in a call centre of a customer care service. The purpose of the analysis is to hypothesize themes about problems and complaints discussed in the conversation. Themes are defined by the application documentation topics. A conversation may contain mentions that are irrelevant for the application purpose ...

INTRODUCTION This paper introduces medical educators to the field of conversation analysis (CA) and its contributions to the understanding of the doctor-patient relationship. THE CONVERSATION ANALYSIS APPROACH Conversation analysis attempts to build bridges both to the ethnographic and the coding and quantitative studies of medical interviews, but examines the medical interview as an arena of...

While many aspects of tutoring have been identified and studied, off topic conversation has been largely ignored. In this paper, off topic conversation during 50 hours of one-to-one expert tutoring sessions was analyzed. Two distinct methodologies (Dialogue Move occurrence and LIWC analysis) were used to determine the anatomy of off topic conversation. Both analyses revealed that the expected s...