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.

翻译：气候变化议题尽管紧迫，但在自然语言处理领域受到的关注有限。活动家与政策制定者需要借助NLP工具，将庞大且快速增长的非结构化气候文本报告有效处理为结构化形式。为应对这一挑战，我们引入了两个大规模气候问卷数据集，并利用其现有结构训练自监督模型。通过实验，我们证明这些模型能够泛化到训练中未见过的不同组织类型的气候披露信息。随后，我们在人类初步研究中利用这些模型，将非结构化气候文档中的文本与半结构化问卷进行对齐。最后，为支持气候领域的进一步NLP研究，我们引入了一个现有气候文本分类数据集基准，以更有效地评估和对比现有模型。