Climate change is threatening human health in unprecedented orders and many ways. These threats are expected to grow unless effective and evidence-based policies are developed and acted upon to minimize or eliminate them. Attaining such a task requires the highest degree of the flow of knowledge from science into policy. The multidisciplinary, location-specific, and vastness of published science makes it challenging to keep track of novel work in this area, as well as making the traditional knowledge synthesis methods inefficient in infusing science into policy. To this end, we consider developing multiple domain-specific language models (LMs) with different variations from Climate- and Health-related information, which can serve as a foundational step toward capturing available knowledge to enable solving different tasks, such as detecting similarities between climate- and health-related concepts, fact-checking, relation extraction, evidence of health effects to policy text generation, and more. To our knowledge, this is the first work that proposes developing multiple domain-specific language models for the considered domains. We will make the developed models, resources, and codebase available for the researchers.
翻译:气候变化以前所未有的秩序和多种方式威胁着人类健康。除非制定有效和循证的政策,并采取行动尽量减少或消除这些威胁,否则这些威胁将会增加。要完成这一任务,就需要从科学到政策的知识流达到最高水平。多学科、针对具体地点和广泛的出版科学使得跟踪这一领域的新工作成为挑战,并使传统知识综合方法在将科学纳入政策时效率低下。为此,我们考虑开发多种特定领域的语言模型,这些模型与气候和健康相关信息不同,可以作为获取现有知识的基础步骤,从而解决不同的任务,例如发现与气候和健康有关的概念之间的相似点、事实检查、相关提取、健康影响证据与政策文本的产生,等等。据我们所知,这是首次提议为考虑的领域开发多个特定领域语言模型的工作。我们将为研究人员提供已开发的模型、资源和代码库。