Conversational Markers of Constructive Discussions

Proceedings of NAACL-HLT 2016, pages 568–578, San Diego, California, June 12-17, 2016. c⃝2016 Association for Computational Linguistics Conversational Markers of Constructive Discussions Vlad Niculae and Cristian Danescu-Niculescu-Mizil {vlad|cristian}@cs.cornell.edu Cornell University Abstract Group discussions are essential for organiz- ing every aspect of modern life, from faculty meetings to senate debates, from grant review panels to papal conclaves. While costly in terms of time and organization effort, group discussions are commonly seen as a way of reaching better decisions compared to solu- tions that do not require coordination between the individuals (e.g. voting)—through discus- sion, the sum becomes greater than the parts. However, this assumption is not irrefutable: anecdotal evidence of wasteful discussions abounds, and in our own experiments we find that over 30% of discussions are unproductive. We propose a framework for analyzing con- versational dynamics in order to determine whether a given task-oriented discussion is worth having or not. We exploit conversa- tional patterns reflecting the flow of ideas and the balance between the participants, as well as their linguistic choices. We apply this framework to conversations naturally occur- ring in an online collaborative world explo- ration game developed and deployed to sup- port this research. Using this setting, we show that linguistic cues and conversational patterns extracted from the first 20 seconds of a team discussion are predictive of whether it will be a wasteful or a productive one. 1 Introduction Working in teams is a common strategy for decision making and problem solving, as building on effec- tive social interaction and on the abilities of each member can enable a team to outperform lone in- dividuals. Evidence shows that teams often per- form better than individuals (Williams and Stern- berg, 1988) and even have high chances of reaching correct answers when all team members were pre- viously wrong (Laughlin and Adamopoulos, 1980). Furthermore, team performance is not a factor of in- dividual intelligence, but of collective intelligence (Woolley et al., 2010), with interpersonal interac- tions and emotional intelligence playing an impor- tant role (Jordan et al., 2002). Yet, as most people can attest from experience, team interaction is not always smooth, and poor co- ordination can lead to unproductive meetings and wasted time. In fact, Romano Jr and Nunamaker Jr (2001) report that one third of work-related meet- ings in the U. S. are considered unproductive, while a 2005 Microsoft employee survey reports that 69% of meetings are ineffective.1 As such, many grow cynical of meetings. Computational methods with the ability to reli- ably recognize unproductive discussions could have an important impact on our society. Ideally, such a system could provide actionable information as a discussion progresses, indicating whether it is likely to turn out to be productive, rather than a waste of time. In this paper we focus on the conversational aspects of productive interactions and take the fol- lowing steps: • introduce a constructiveness framework that al- lows us to characterize teams where discussion enables better performance than the individu- als could reach, and, conversely, teams …

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