面向非结构化气候报告的气候问卷解答
Towards Answering Climate Questionnaires from Unstructured Climate Reports
AI总结：
针对气候变化NLP关注不足及报告非结构化问题，研究构建两个大规模气候问卷数据集训练自监督模型，经实验验证其能跨组织类型泛化，并在人工试点中实现文本对齐，同时提供基准支持后续研究。
摘要：
尽管气候变化（CC）议题十分紧迫，但其在NLP中受到的关注有限。活动家和政策制定者需要NLP工具，以将大量且快速增长的非结构化文本气候报告有效处理为结构化形式。为应对这一挑战，我们引入了两个新的大规模气候问卷数据集，并利用其现有结构来训练自监督模型。我们通过实验表明，这些模型能够学习泛化至与训练时所见不同类型的组织的气候披露信息。随后，在一个人工试点研究中，我们利用这些模型帮助将非结构化气候文档中的文本与半结构化问卷相对齐。最后，为了支持气候领域的进一步NLP研究，我们引入了一个由现有气候文本分类数据集组成的基准，以更好地评估和比较现有模型。
英文摘要：
The topic of Climate Change (CC) has received limited attention in NLP despite its urgency. Activists and policymakers need NLP tools to effectively process the vast and rapidly growing unstructured textual climate reports into structured form. To tackle this challenge we introduce two new large-scale climate questionnaire datasets and use their existing structure to train self-supervised models. We conduct experiments to show that these models can learn to generalize to climate disclosures of different organizations types than seen during training. We then use these models to help align texts from unstructured climate documents to the semi-structured questionnaires in a human pilot study. Finally, to support further NLP research in the climate domain we introduce a benchmark of existing climate text classification datasets to better evaluate and compare existing models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
