task_categories:text-classification annotations_creators:other language_creators:other multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en arxiv:2301.04253 region:us
The topic of Climate Change (CC) has received limited attention in NLP despite its real world urgency.
Activists and policy-makers need NLP tools in order to effectively process the vast and rapidly growing textual data produced on CC.
Their utility, however, primarily depends on whether the current state-of-the-art models can generalize across various tasks in the CC domain.
In order to address this gap, we introduce Climate Change Benchmark (Climabench), a benchmark collection of existing disparate datasets for evaluating model performance across a diverse set of CC NLU tasks systematically.
Further, we enhance the benchmark by releasing two large-scale labelled text classification and question-answering datasets curated from publicly available environmental disclosures.
Lastly, we provide an analysis of several generic and CC-oriented models answering whether fine-tuning on domain text offers any improvements across these tasks. We hope this work provides a standard assessment tool for research on CC text data.
@misc{laud2023Climabench,
      title={ClimaBench: A Benchmark Dataset For Climate Change Text Understanding in English}, 
      author={Tanmay Laud and Daniel Spokoyny and Tom Corringham and Taylor Berg-Kirkpatrick},
      year={2023},
      eprint={2301.04253},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
---
annotations_creators:
  - other
language_creators:
  - other
language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
paperswithcode_id: climabench
pretty_name: "ClimaBench: A Benchmark Dataset For Climate Change Text Understanding in English"

config_names:
  - climate_stance
  - climate_eng
  - climate_fever
  - climatext
  - clima_insurance
  - clima_insurance_plus
  - clima_cdp
  - clima_qa
---

### Citation Information

```
@misc{spokoyny2023answering,
    title={Towards Answering Climate Questionnaires from Unstructured Climate Reports},
    author={Daniel Spokoyny and Tanmay Laud and Tom Corringham and Taylor Berg-Kirkpatrick},
    year={2023},
    eprint={2301.04253},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

```