Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning

The Visual Dialogue task requires an agent to engage in a conversation about an image with a human. It represents an extension of the Visual Question Answering task in that the agent needs to answer a question about an image, but it needs to do so in light of the previous dialogue that has taken place. The key challenge in Visual Dialogue is thus maintaining a consistent, and natural dialogue while continuing to answer questions correctly. We present a novel approach that combines Reinforcement Learning and Generative Adversarial Networks (GANs) to generate more human-like responses to questions. The GAN helps overcome the relative paucity of training data, and the tendency of the typical MLE-based approach to generate overly terse answers. Critically, the GAN is tightly integrated into the attention mechanism that generates human-interpretable reasons for each answer. This means that the discriminative model of the GAN has the task of assessing whether a candidate answer is generated by a human or not, given the provided reason. This is significant because it drives the generative model to produce high quality answers that are well supported by the associated reasoning. The method also generates the state-of-the-art results on the primary benchmark

Effective classroom practice: a mixed-method study of influences and outcomes: a research paper

This brief paper reports findings from a two-year research project, funded by the ESRC, which identified, described and analyzed variation in effective primary and secondary school teachers’ classroom practice. The study also explored these practices in relation to different school contexts and teachers’ professional life phases in order to draw out relevant implications for policy and practice

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Comparative analysis of spring flood risk reduction measures in Alaska, United States and the Sakha Republic, Russia

Thesis (Ph.D.) University of Alaska Fairbanks, 2017River ice thaw and breakup are an annual springtime phenomena in the North. Depending on regional weather patterns and river morphology, breakups can result in catastrophic floods in exposed and vulnerable communities. Breakup flood risk is especially high in rural and remote northern communities, where flood relief and recovery are complicated by unique geographical and climatological features, and limited physical and communication infrastructure. Proactive spring flood management would significantly minimize the adverse impacts of spring floods. Proactive flood management entails flood risk reduction through advances in ice jam and flood prevention, forecasting and mitigation, and community preparedness. With the goal to identify best practices in spring flood risk reduction, I conducted a comparative case study between two flood-prone communities, Galena in Alaska, United States and Edeytsy in the Sakha Republic, Russia. Within a week from each other, Galena and Edeytsy sustained major floods in May 2013. Methods included focus groups with the representatives from flood managing agencies, surveys of families impacted by the 2013 floods, observations on site, and archival review. Comparative parameters of the study included natural and human causes of spring floods, effectiveness of spring flood mitigation and preparedness strategies, and the role of interagency communication and cooperation in flood risk reduction. The analysis revealed that spring flood risk in Galena and Edeytsy results from complex interactions among a series of natural processes and human actions that generate conditions of hazard, exposure, and vulnerability. Therefore, flood risk in Galena and Edeytsy can be reduced by managing conditions of ice-jam floods, and decreasing exposure and vulnerability of the at-risk populations. Implementing the Pressure and Release model to analyze the vulnerability progression of Edeytsy and Galena points to common root causes at the two research sites, including colonial heritage, unequal distribution of resources and power, top-down governance, and limited inclusion of local communities in the decision-making process. To construct an appropriate flood risk reduction framework it is important to establish a dialogue among the diverse stakeholders on potential solutions, arriving at a range of top-down and bottom-up initiatives and in conjunction selecting the appropriate strategies. Both communities have progressed in terms of greater awareness of the hazard, reduction in vulnerabilities, and a shift to more reliance on shelter-in-place. However, in neither community have needed improvements in levee protection been completed. Dialogue between outside authorities and the community begins earlier and is more intensive for Edeytsy, perhaps accounting for Edeytsy's more favorable rating of risk management and response than Galena's