This paper presents an overview of a robust, broad-coverage, and application-independent natural language generation system. It demonstrates how the different language generation components function within a multilingual Machine Translation (MT) system, using the languages that we are currently working on (English, Spanish, Japanese, and Chinese). Section 1 provides a system description. Section 2 focuses on the generation components and their core set of rules. Section 3 describes an additional layer of generation rules included to address applicationspecific issues. Section 4 provides a brief description of the evaluation method and results for the MT system of which our generation components are a part.
This paper examines the current state of knowledge focusing on the second-order impacts of the COVID-19 pandemic through a geospatial lens. The purpose is twofold: (1) present a global programme -Cities' COVID Mitigation Mapping (C2M2) programme -focusing on urban areas that explores second-order impacts through the use of geospatial tools and technologies, and (2) identify and assess the emerging literature on second-order impacts using geospatial data and analysis to support this project. Effects of the pandemic are rapidly unfolding across the world; however, an assessment of the literature reveals that second-order impacts of COVID-19 are seasonal, spatial, and scalar across multiple thematic areas includ-ing the economy, environmental health sector, education, and migration/mobility. Successive waves of the pandemic are continuing to be met with specific public health measures (e.g. lockdowns, travel restrictions, social distancing guidance, mandates for the use of personal protective equipment) that will have longterm impacts on vulnerable populations. A literature review was conducted to identify how the pandemic's second-order impacts derived from geospatial data and analysis can provide the basis for using geospatial data to study vulnerable urban populations more generally. This review reveals a gap in the literature, with far more articles emphasizing geospatial approaches to assess first-order impacts and alimited number of articles focused on geospatial approaches investigating second-order impacts. Nonetheless, this nascent literature provides the basis for designing approaches with local partners and by local and regional governments to apply geospatial data and methodologies to the development of mitigation strategies to prioritize limited resources to minimize the long-term consequences of COVID-19.
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