In this paper, we analyze complex gaze tracking data in a collaborative task and apply machine learning models to automatically predict skill-level differences between participants. Specifically, we present findings that address the two primary challenges for this prediction task: (1) extracting meaningful features from the gaze information, and (2) casting the prediction task as a machine learning (ML) problem. The results show that our approach based on profile hidden Markov models are up to 96% accurate and can make the determination as fast as one minute into the collaboration, with only 5% of gaze observations registered. We also provide a qualitative analysis of gaze patterns that reveal the relative expertise level of the paired users in a collaborative learning user study.

Advances in multimedia technologies have enabled the creation of huge archives of audio-video recordings of meetings, and there is burgeoning interest in developing meeting browsers to help users better leverage these archives. A recent study has shown that extractive summaries provide a more efficient way of navigating meeting content than simply reading through the transcript and using the audio-video record, or navigating via keyword search (Murray, 2007). The extractive summary technique identifies informative dialogue acts to generate general purpose summaries. These summaries can still be lengthy. Recently, we have developed a decision-focused summarization system that presents only 1-2% of the recordings related to decision making. In this paper, we describe a task-based evaluation in which we compare the decision-focused summaries to the general purpose summaries. Our results indicate that the more focused summaries help users perform the decision debriefing task more effectively and improve perceived efficiency. In addition, this study also investigates the effect of automatic summaries and transcription on task effectiveness, report quality, and users' perceptions of task success.

In this paper, we explore a novel way to leverage audio information for unsupervised segmentation of multiparty dialogue. Our system which segments directly on patterns derived from audio sources is evaluated with previous work that segments on lexical patterns found in transcripts. We examine the effectiveness of both systems on recovering a two-layer structure of meeting dialogue. We demonstrate that the audio-based system performs significantly better than the word-based system on this task. In particular, it effectively recover segments of off-topic discussion. Results are encouraging as the audio information used in the system can be obtained in near real time and with absence of manual and ASR transcripts. It is particularly desirable when a system has to be operated online, or in unfamiliar domains and languages.

This study concerns how to segment a scenario-driven multiparty dialogue and how to label these segments automatically. We apply approaches that have been proposed for identifying topic boundaries at a coarser level to the problem of identifying agenda-based topic boundaries in scenario-based meetings. We also develop conditional models to classify segments into topic classes. Experiments in topic segmentation show that a supervised classification approach that combines lexical and conversational features outperforms the unsupervised lexical chain-based approach, achieving 20% and 12% improvement on segmentating top-level and sub-topic segments respectively. Experiments in topic classification suggest that it is possible to automatically categorize segments into appropriate topic classes given only the transcripts. Training with features selected using the Log Likelihood ratio improves the results by 13.3%.