A Dataset for Detecting Real-World Environmental Claims

09/01/2022
by   Dominik Stammbach, et al.
1

In this paper, we introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies. We train and release baseline models for detecting environmental claims using this new dataset. We further preview potential applications of our dataset: We use our fine-tuned model to detect environmental claims made in answer sections of quarterly earning calls between 2012 and 2020 – and we find that the amount of environmental claims steadily increased since the Paris Agreement in 2015.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/22/2023

AVeriTeC: A dataset for real-world claim verification with evidence from the web

Existing datasets for automated fact-checking have substantial limitatio...
research
06/07/2021

COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic

We introduce a FEVER-like dataset COVID-Fact of 4,086 claims concerning ...
research
10/29/2019

A Note About: Critical Review of BugSwarm for Fault Localization and Program Repair

Datasets play an important role in the advancement of software tools and...
research
09/19/2023

Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learning

Unsupported and unfalsifiable claims we encounter in our daily lives can...
research
02/02/2023

Reconsidering Fascicles in Soft Pneumatic Actuator Packs

This paper discusses and contests the claims of “Soft Pneumatic Actuator...
research
02/18/2020

Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims

We present a study on the efficacy of adversarial training on transforme...
research
02/11/2022

Tracking environmental policy changes in the Brazilian Federal Official Gazette

Even though most of its energy generation comes from renewable sources, ...

Please sign up or login with your details

Forgot password? Click here to reset