Distance Dependent Chinese Restaurant Processes

We develop the distance dependent Chinese restaurant process (CRP), a flexible class of distributions over partitions that allows for non-exchangeability. This class can be used to model many kinds of dependencies between data in infinite clustering models, including dependencies across time or space. We examine the properties of the distance dependent CRP, discuss its connections to Bayesian nonparametric mixture models, and derive a Gibbs sampler for both observed and mixture settings. We study its performance with three text corpora. We show that relaxing the assumption of exchangeability with distance dependent CRPs can provide a better fit to sequential data. We also show its alternative formulation of the traditional CRP leads to a faster-mixing Gibbs sampling algorithm than the one based on the original formulation

Immigration and the neighborhood

What impact does immigration have on neighborhood dynamics? Within metropolitan areas, the authors find that housing values have grown relatively more slowly in neighborhoods of immigrant settlement. They propose three nonexclusive explanations: changes in housing quality, reverse causality, or the hypothesis that natives find immigrant neighbors relatively less attractive (native flight). To instrument for the actual number of new immigrants, the authors deploy a geographic diffusion model that predicts the number of new immigrants in a neighborhood using lagged densities of the foreign-born in surrounding neighborhoods. Subject to the validity of their instruments, the evidence is consistent with a causal interpretation of an impact from growing immigration density to native flight and relatively slower housing price appreciation. Further evidence indicates that these results may be driven more by the demand for residential segregation based on race and education than by foreignness per se.Immigrants

Self-Supervised and Controlled Multi-Document Opinion Summarization

We address the problem of unsupervised abstractive summarization of collections of user generated reviews with self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss. We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries.Finally, we extend the Transformer architecture to allow for multiple reviews as input. Our benchmarks on two datasets against graph-based and recent neural abstractive unsupervised models show that our proposed method generates summaries with a superior quality and relevance.This is confirmed in our human evaluation which focuses explicitly on the faithfulness of generated summaries We also provide an ablation study, which shows the importance of the control setup in controlling hallucinations and achieve high sentiment and topic alignment of the summaries with the input reviews.Comment: 18 pages including 5 pages appendi

Collateral Damage: Trade Disruption and the Economic Impact of War

Conventional wisdom in economic history suggests that conflict between countries can be enormously disruptive of economic activity, especially international trade. Yet nothing is known empirically about these effects in large samples. We study the effects of war on bilateral trade for almost all countries with available data extending back to 1870. Using the gravity model, we estimate the contemporaneous and lagged effects of wars on the trade of belligerent nations and neutrals, controlling for other determinants of trade. We find large and persistent impacts of wars on trade, and hence on national and global economic welfare. A rough accounting indicates that such costs might be of the same order of magnitude as the """"direct"""" costs of war, such as lost human capital, as illustrated by case studies of World War I and World War II.trade

Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies

Advances in artificial intelligence have renewed interest in conversational agents. So-called chatbots have reached maturity for industrial applications. German insurance companies are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies by determining which classes of agents are of interest to insurance companies, finding relevant use cases and requirements, and developing a prototype for an exemplary insurance scenario. Based on this approach, we derive key findings for conversational agent implementation in insurance companies.Comment: 12 pages, 6 figure, accepted for presentation at The International Conference on Agents and Artificial Intelligence 2019 (ICAART 2019