Intentionality and the Role of Labels in Categorization

Language has been shown to influence the ability to formcategories. Here we investigate whether linguistic labels areprivileged compared to other types of cues (e.g., numbers orsymbols), and whether labels exert their effects regardless ofwhether they are introduced intentionally. In a categorizationtask, we found that adults were more likely to use labels to de-termine category boundaries compared to numbers or symbols,and that these effects persisted in all intentionality manipula-tions. These findings suggest that labels have a powerful effecton categorization compared to other cues; most strikingly, la-bels (but not other cues) are used during categorization evenwhen people are specifically asked to ignore them. These re-sults provide novel support for the position that labels indicatecategory membership

Team Communication as a Collaborative Process

Communication channels can reveal a great deal of information about the effectiveness of a team. This is particularly relevant for teams operating in performance settings such as medical groups, military squads, and mixed human-robot teams. Currently, it is not known how various factors including coordination strategy, speaker role, and time pressure affect communication in collaborative tasks. The purpose of this paper is to systematically explore how these factors interact with team discourse in order to better understand effective communication patterns. In our analysis of a corpus of remote task-oriented dialogue (CReST corpus), we found that a variety of linguistic- and dialogue-level features were influenced by time pressure, speaker role, and team effectiveness. We also found that effective teams had a higher speech rate, and used specific grounding strategies to improve efficiency and coordination under time pressure. These results inform our understanding of the various factors that influence team communication, and highlight ways in which effective teams overcome constraints on their communication channels

The Pragmatic Parliament: A Framework for Socially-Appropriate UtteranceSelection in Artificial Agents

One of the hallmarks of human natural language (NL) inter-action is the ability for people to balance a variety of so-cial and communicative goals when choosing how to realizetheir speech actions. These goals can include pragmatic criteriasuch as correctness, informativeness, and brevity (i.e., Griceanconversational maxims) or social factors such as politeness.However, there currently does not exist a general algorithmicmethod to explicitly modulate language generated by artificialagents based on an arbitrary number of pragmatic and socialcriteria. We propose a novel method to accomplish this task,in which rankings of candidate utterances by different prag-matic or social criteria are fused by use of a voting algorithm.We then give a proof-of-concept demonstration of the applica-tion of this method in the context of directive generation forhuman-robot interaction