Books are silent ambassadors: The Noma award for the translation of Japanese literature

Summary Now more than ever good translations are proving to be vitally important to fulfilling Shoichi Noma’s mission of advancing good relationships between cultures. With the emergence of dozens of world-class Japanese writers, the translators of Japanese literature have acquired a new significance and a new importance. No longer are they required to simply handle the language like an exotic and precious commodity but they must prepare a writer’s work to compete with literature from around the globe and let others understand the issues at their very heart of the Japanese people. Indeed, the selection committee’s choice of Murakami’s Wind-Up Bird Chronicle is an apt example of how literature at its very best can span cultural divides to promote mutual understanding of very complicated and important world issues. Written for Japanese to help them understand the devastation of war, it is a rare and timely book that confronts Japan’s experience head on. Ironically, Noma’s idea that books are silent ambassadors, an idea born from agony and defeat, so many years later is helping the world come together towards a unified vision of peace and understanding. “Literature has been described as a means of exchange between the hearts of people. Literary translation, then, is the building of bridges so that all peoples of the world can interact in this way.” Ye Weiqu, Chinese Institute of Social Studies.     

Analysis of Statistical Question Classification for Fact-Based Questions

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.     

The Changing Research Data Landscape and the Experiences of Ethics Review Board Chairs: Implications for Library Practice and Partnerships

Academic libraries have to a large extent taken the lead in facilitating new approaches to research data management, but changes to the research data landscape have had an impact on numerous areas of academic work, including ethics review. Using interpretive phenomenological analysis of interviews with chairs of Canadian research ethics boards, this study explores how ethics review boards have experienced changes to data policy and related technologies in order to describe the ethical implications of new approaches to data management and to explore ways in which the library, ethics review boards, and other campus partners might harmonize efforts to support emerging data practices. While ethics review boards in Canada are keenly aware of open data policies, data publishing in practice is still nascent. There is uncertainty about the adoption of changing technologies for research and their impacts on privacy protection. Where responsibility lies for addressing these uncertainties is often unclear. Academic libraries and research ethics boards are well-suited to engage in mutual knowledge transfer and to integrate data management planning and ethics review processes. Institutional-level oversight that includes all campus departments impacted by changes to the research data landscape may facilitate improved communication and reduce role ambiguity.