# Towards Answering Climate Questionnaires from Unstructured Climate Reports

arXiv: https://arxiv.org/abs/2301.04253 · HTML: https://mlsanity.com/paper/2301.04253/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 48 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://mlsanity.com/paper/2012.00614/) (2020)
- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [ClimaText: A Dataset for Climate Change Topic Detection](https://mlsanity.com/paper/2012.00483/) (2020)
- [Analyzing Sustainability Reports Using Natural Language Processing](https://mlsanity.com/paper/2011.08073/) (2020)
- [NLP for Climate Policy: Creating a Knowledge Platform for Holistic and Effective Climate Action](https://mlsanity.com/paper/2105.05621/) (2021)

## Built on by (most cited first, 10 of 10)

- [WeatherQA: Can Multimodal Language Models Reason about Severe Weather?](https://mlsanity.com/paper/2406.11217/) (2024)
- [Learning to Rewrite: Generalized LLM-Generated Text Detection](https://mlsanity.com/paper/2408.04237/) (2024)
- [Don't Think Twice! Over-Reasoning Impairs Confidence Calibration](https://mlsanity.com/paper/2508.15050/) (2025)
- [A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation](https://mlsanity.com/paper/2504.17200/) (2025)
- [Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change](https://mlsanity.com/paper/2505.18653/) (2025)
- [Identifying Climate Targets in National Laws and Policies using Machine Learning](https://mlsanity.com/paper/2404.02822/) (2024)
- [Integrating Expert Labels into LLM-based Emission Goal Detection: Example Selection vs Automatic Prompt Design](https://mlsanity.com/paper/2412.06432/) (2024)
- [A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering](https://mlsanity.com/paper/2606.00402/) (2026)
- [Execution-grounded evaluation reveals hidden failures in language-model calculations for environmental science](https://mlsanity.com/paper/2608.18726/) (2026)
- [Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining](https://mlsanity.com/paper/2609.07798/) (2026)

## Abstract (arXiv)

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.