MAILEX: Email Event and Argument Extraction

In this work, we present the first dataset, \dataset, for performing event extraction from conversational email threads. To this end, we first proposed a new taxonomy covering 10 event types and 76 arguments in the email domain. Our final dataset includes $\sim$4K emails annotated with $\sim$9K event instances. To understand the task challenges, we conducted a series of experiments comparing two commonly-seen lines of approaches for event extraction, i.e., sequence labeling and generative end-to-end extraction (including few-shot GPT-3.5). Our results showed that the task of email event extraction is far from being addressed, due to challenges lying in, e.g., extracting non-continuous, shared trigger spans, extracting non-named entity arguments, and modeling the email conversational history. Our work thus suggests more investigations in this domain-specific event extraction task in the future.\footnote{The source code and dataset can be obtained from \url{https://github.com/salokr/Email-Event-Extraction}

ONE ENVIRONMENT, ONE HEALTH

Nebraska’s place as an international agricultural epicenter is important for feeding a hungry world, but the work that makes that position possible is truly incredible. Thanks to research conducted at the University of Nebraska–Lincoln by many of the world’s finest scientists, discoveries are being made that improve the health of all kingdoms – plant, animal, human and the natural environment. The UNL Institute of Agriculture and Natural Resources scientists are Growing a Healthy Future through their work in laboratories on campus and in the laboratories of the world – fields, rivers, pastures, feedlots, swine facilities, hen houses, zoos and public health clinics. They are the pioneers who are learning to prevent and cure plant, animal and human diseases and protect the biodiversity of the natural environment. We are fortunate to have outstanding programs and facilities in addition to outstanding people, among them: the Nebraska Center for Virology; Nebraska Center for Prevention of Obesity Diseases; UNL Center for Biotechnology; Doctor of Plant Health program; Gut Function Initiative; Great Plains Veterinary Educational Center; School of Veterinary Medicine and Biomedical Sciences; and the Nebraska Veterinary Diagnostic Center. The scientists in these programs and facilities are protecting the health and well-being of all. University of Nebraska scientists from all four campuses and the Nebraska College of Technical Agriculture, as well as other educational institutions are reaching out to one another, bringing in scientists from around the world and working together to learn more about the biology shared by all living things. They are using that knowledge to grow a healthier future for all of us. In this 2015 Strategic Discussions for Nebraska publication, you will find stories that explain the importance of One Environment, One Health: Animals, Plants and Us. Many stories refer to the concept of One Health, which was first articulated in the early 2000s by the United States veterinary community. Concern that animal disease might jump from animal to human initiated the One Health concept, which explains that all kingdoms are interlinked. As you read this publication, you will learn about astounding progress in solving the puzzles of disease, saving crops, lives and billions of dollars in economic activity. A friend’s daughter had a third-grade teacher who taped a memorable phrase to the wall of the classroom: “Through hard work and perseverance, you have the potential to achieve excellence.” That’s quite a goal for a class of eight-year-olds, but here at the University of Nebraska–Lincoln, our hard work and perseverance are reaping excellence that is improving the health of the environment, plants, animals – and us

Prediction-error in the context of real social relationships modulates reward system activity.

The human reward system is sensitive to both social (e.g., validation) and non-social rewards (e.g., money) and is likely integral for relationship development and reputation building. However, data is sparse on the question of whether implicit social reward processing meaningfully contributes to explicit social representations such as trust and attachment security in pre-existing relationships. This event-related fMRI experiment examined reward system prediction-error activity in response to a potent social reward-social validation-and this activity's relation to both attachment security and trust in the context of real romantic relationships. During the experiment, participants' expectations for their romantic partners' positive regard of them were confirmed (validated) or violated, in either positive or negative directions. Primary analyses were conducted using predefined regions of interest, the locations of which were taken from previously published research. Results indicate that activity for mid-brain and striatal reward system regions of interest was modulated by social reward expectation violation in ways consistent with prior research on reward prediction-error. Additionally, activity in the striatum during viewing of disconfirmatory information was associated with both increases in post-scan reports of attachment anxiety and decreases in post-scan trust, a finding that follows directly from representational models of attachment and trust

Modeling Strategic Use of Human Computer Interfaces with Novel Hidden Markov Models

Immersive software tools are virtual environments designed to give their users an augmented view of real-world data and ways of manipulating that data. As virtual environments, every action users make while interacting with these tools can be carefully logged, as can the state of the software and the information it presents to the user, giving these actions context. This data provides a high-resolution lens through which dynamic cognitive and behavioral processes can be viewed. In this report, we describe new methods for the analysis and interpretation of such data, utilizing a novel implementation of the Beta Process Hidden Markov Model (BP-HMM) for analysis of software activity logs. We further report the results of a preliminary study designed to establish the validity of our modeling approach. A group of 20 participants were asked to play a simple computer game, instrumented to log every interaction with the interface. Participants had no previous experience with the game’s functionality or rules, so the activity logs collected during their naïve interactions capture patterns of exploratory behavior and skill acquisition as they attempted to learn the rules of the game. Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload. We jointly modeled the activity log sequences collected from all participants using the BP-HMM approach, identifying a global library of activity patterns representative of the collective behavior of all the participants. Analyses show systematic relationships between both pre- and post-task questionnaires, self-reported approaches to analytic problem solving, and metrics extracted from the BP-HMM decomposition. Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit