Retrieve and Refine: Improved Sequence Generation Models For Dialogue

Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging. Retrieval models on the other hand can surface interesting responses, but are restricted to the given retrieval set leading to erroneous replies that cannot be tuned to the specific context. In this work we develop a model that combines the two approaches to avoid both their deficiencies: first retrieve a response and then refine it -- the final sequence generator treating the retrieval as additional context. We show on the recent CONVAI2 challenge task our approach produces responses superior to both standard retrieval and generation models in human evaluations

Wellness from Diabetes: Community Health and Diabetes Assessment

The Republic of the Marshall Islands (RMI) is highly prevalent in type 2 diabetes mellitus (T2DM) with a prevalence rate of 37.37%, the highest in the world. T2DM dominates Majuro, the country’s capital, as a leading cause of mortality and morbidity, despite efforts of health care workers, local community organizations, and government. Income and education are social determinants of health. The correlations between good health and high income, and between good health and high education level, are positive. However, there is a continuous growth of T2DM incidence and prevalence on Majuro. Therefore, we hypothesized that there is no significant difference between healthful dietary and exercise practices of two groups of people on Majuro, RMI: those with high income and high education levels, and those with low income and low education levels. Community-based research conducted on Majuro helped test our hypothesis and gain knowledge of necessary steps to reverse this epidemic. During beginning stages of our research, related literature on diabetes, social determinants of health, and research methods were reviewed. To acquire qualitative data, focus group discussions (FGDs) and key informant interviews (KIIs) were conducted. FGDs were held with people grouped according to profession (health, education, community). With the KIIs, key members deeply involved or active in the community were interviewed one-on-one. The bulk of our quantitative data will be gathered by surveys on basic demographics, economics, and health-related perceptions. In collaboration with the Ministry of Health and local organizations, 400 surveys will be administered in Marshallese and English, and collected

Physical and Digital Disengagement Behaviours in the University Classroom

Abstract to be adde

Reachability Analysis for Lexicase Selection via Community Assembly Graphs

Fitness landscapes have historically been a powerful tool for analyzing the search space explored by evolutionary algorithms. In particular, they facilitate understanding how easily reachable an optimal solution is from a given starting point. However, simple fitness landscapes are inappropriate for analyzing the search space seen by selection schemes like lexicase selection in which the outcome of selection depends heavily on the current contents of the population (i.e. selection schemes with complex ecological dynamics). Here, we propose borrowing a tool from ecology to solve this problem: community assembly graphs. We demonstrate a simple proof-of-concept for this approach on an NK Landscape where we have perfect information. We then demonstrate that this approach can be successfully applied to a complex genetic programming problem. While further research is necessary to understand how to best use this tool, we believe it will be a valuable addition to our toolkit and facilitate analyses that were previously impossible