@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}
}

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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  - 

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