Individual and Domain Adaptation in Sentence Planning for Dialogue

One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations

Subtree power analysis finds optimal species for comparative genomics

Sequence comparison across multiple organisms aids in the detection of regions under selection. However, resource limitations require a prioritization of genomes to be sequenced. This prioritization should be grounded in two considerations: the lineal scope encompassing the biological phenomena of interest, and the optimal species within that scope for detecting functional elements. We introduce a statistical framework for optimal species subset selection, based on maximizing power to detect conserved sites. In a study of vertebrate species, we show that the optimal species subset is not in general the most evolutionarily diverged subset. Our results suggest that marsupials are prime sequencing candidates.Comment: 16 pages, 3 figures, 3 table

Speech-plans: Generating evaluative responses in spoken dialogue

Recent work on evaluation of spoken dialogue systems indicates that better algorithms are needed for the presentation of complex information in speech. Current dialogue systems often rely on presenting sets of options and their attributes sequentially. This places a large memory burden on users, who have to remember complex trade-offs between multiple options and their attributes. To address these problems we build on previous work using multiattribute decision theory to devise speech-planning algorithms that present usertailored summaries, comparisons and recommendations that allow users to focus on critical differences between options and their attributes. We discuss the differences between speech and text planning that result from the particular demands of the speech situation.

Methods for anticipating governance breakdown and violent conflict

In this paper, authors Sarah Bressan, Håvard Mokleiv Nygård, and Dominic Seefeldt present the evolution and state of the art of both quantitative forecasting and scenario-based foresight methods that can be applied to help prevent governance breakdown and violent conflict in Europe’s neighbourhood. In the quantitative section, they describe the different phases of conflict forecasting in political science and outline which methodological gaps EU-LISTCO’s quantitative sub-national prediction tool will address to forecast tipping points for violent conflict and governance breakdown. The qualitative section explains EU-LISTCO’s scenario-based foresight methodology for identifying potential tipping points. After comparing both approaches, the authors discuss opportunities for methodological advancements across the boundaries of quantitative forecasting and scenario-based foresight, as well as how they can inform the design of strategic policy options

Crime scripting: A systematic review

The file attached to this record is the author's final peer reviewed version.More than two decades after the publication of Cornish’s seminal work about the script-theoretic approach to crime analysis, this article examines how the concept has been applied in our community. The study provides evidence confirming that the approach is increasingly popular; and takes stock of crime scripting practices through a systematic review of over one hundred scripts published between 1994 and 2018. The results offer the first comprehensive picture of this approach, and highlights new directions for those interested in using data from cyber-systems and the Internet of Things to develop effective situational crime prevention measures