In this section
@inproceedings{Kotonya:2020:v1/2020.emnlp-main.623,
author = {Kotonya, N and Toni, F},
doi = {v1/2020.emnlp-main.623},
pages = {7740--7754},
publisher = {ACL},
title = {Explainable Automated Fact-Checking for Public Health Claims},
url = {http://dx.doi.org/10.18653/v1/2020.emnlp-main.623},
year = {2020}
}
TY - CPAPER
AB - Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast major-ity of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new datasetPUBHEALTHof 11.8K claims accompanied by journalist crafted, gold standard explanations(i.e., judgments) to support the fact-check la-bels for claims1. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that,by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.
AU - Kotonya,N
AU - Toni,F
DO - v1/2020.emnlp-main.623
EP - 7754
PB - ACL
PY - 2020///
SP - 7740
TI - Explainable Automated Fact-Checking for Public Health Claims
UR - http://dx.doi.org/10.18653/v1/2020.emnlp-main.623
UR - https://www.aclweb.org/anthology/2020.emnlp-main.623/
UR - http://hdl.handle.net/10044/1/83662
ER -
Artificial Intelligence Network
South Kensington Campus
Imperial College London
SW7 2AZ
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