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The topic of Climate Change (CC) has received limited attention in NLPdespite its urgency. Activists and policymakers need NLP tools to effectivelyprocess the vast and rapidly growing unstructured textual climate reports intostructured form. To tackle this challenge we introduce two new large-scaleclimate questionnaire datasets and use their existing structure to trainself-supervised models. We conduct experiments to show that these models canlearn to generalize to climate disclosures of different organizations typesthan seen during training. We then use these models to help align texts fromunstructured climate documents to the semi-structured questionnaires in a humanpilot study. Finally, to support further NLP research in the climate domain weintroduce a benchmark of existing climate text classification datasets tobetter evaluate and compare existing models.

Towards Answering Climate Questionnaires from Unstructured Climate Reports