Towards Answering Climate Questionnaires from Unstructured Climate Reports

• URL: http://arxiv.org/abs/2301.04253v2
• Date: Thu, 27 Jul 2023 18:13:40 GMT
• Title: Towards Answering Climate Questionnaires from Unstructured Climate Reports
• Authors: Daniel Spokoyny, Tanmay Laud, Tom Corringham, Taylor Berg-Kirkpatrick
• Abstract summary: Activists and policymakers need NLP tools to process the vast and rapidly growing unstructured textual climate reports into structured form. We introduce two new large-scale climate questionnaire datasets and use their existing structure to train self-supervised models. We then use these models to help align texts from unstructured climate documents to the semi-structured questionnaires in a human pilot study.
• Score: 26.036105166376284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• ClimaQA: An Automated Evaluation Framework for Climate Foundation Models [38.05357439484919]
  We develop ClimaGen, an automated framework that generates question-answer pairs from graduate textbooks with climate scientists in the loop. We present ClimaQA-Gold, an expert-annotated benchmark dataset alongside ClimaQA-Silver, a large-scale, comprehensive synthetic QA dataset for climate science.
  arXiv  Detail & Related papers  (2024-10-22T05:12:19Z)
• ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change [21.827936253363603]
  This paper introduces ClimateGPT, a model family of domain-specific large language models that synthesize interdisciplinary research on climate change. We trained two 7B models from scratch on a science-oriented dataset of 300B tokens. ClimateGPT-7B, 13B and 70B are continuously pre-trained from Llama2 on a domain-specific dataset of 4.2B tokens.
  arXiv  Detail & Related papers  (2024-01-17T23:29:46Z)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• Climate Change from Large Language Models [7.190384101545232]
  Climate change poses grave challenges, demanding widespread understanding and low-carbon lifestyle awareness. Large language models (LLMs) offer a powerful tool to address this crisis. This paper proposes an automated evaluation framework to assess climate-crisis knowledge.
  arXiv  Detail & Related papers  (2023-12-19T09:26:46Z)
• Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM [77.17254959695218]
  Large Language Models (LLMs) like ChatGPT and Bard have shown impressive conversational abilities and excel in a wide variety of NLP tasks. We propose a light-weight Arabic Mini-ClimateGPT that is built on an open-source LLM and is specifically fine-tuned on a conversational-style instruction tuning Arabic dataset Clima500-Instruct. Our model surpasses the baseline LLM in 88.3% of cases during ChatGPT-based evaluation.
  arXiv  Detail & Related papers  (2023-12-14T22:04:07Z)
• Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks [0.0]
  This paper applies state-of-the-art NLP techniques to achieve the detection of climate change in text corpora. We use transfer learning to fine-tune two transformer models, BERT and ClimateBert.
  arXiv  Detail & Related papers  (2023-03-21T07:25:36Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
  We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
  arXiv  Detail & Related papers  (2022-11-15T14:15:04Z)
• CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims [4.574830585715129]
  We introduce CLIMATE-FEVER, a new dataset for verification of climate change-related claims. We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the textscfever framework.
  arXiv  Detail & Related papers  (2020-12-01T16:32:54Z)
• Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
  In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG) We present this tool and the methodology that we used to develop it in the present article.
  arXiv  Detail & Related papers  (2020-11-03T21:22:42Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.