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✅ How Robust are Fact Checking Systems on Colloquial Claims?. In NAACL-HLT, 2021.
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bckim92/colloquial-claims
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Official PyTorch implementation of our NAACL paper:
Byeongchang Kim*,Hyunwoo Kim*,Seokhee Hong, andGunhee Kim. How Robust are Fact Checking Systems on Colloquial Claims?NAACL-HLT, 2021[Paper] (* equal contribution)
If you use the materials in this repository as part of any published research, we ask you to cite the followingpaper:
@inproceedings{Kim:2021:colloquial,title={How Robust are Fact Checking Systems on Colloquial Claims?},author={Kim, Byeongchang and Kim, Hyunwoo and Hong, Seokhee and Kim, Gunhee},booktitle={NAACL-HLT},year={2021}}
You can download the paper version of our Colloquial Claims dataset via following urls:
[train][valid][test]
You can read and explore the dataset as follows:
importjsonturns= []withopen('colloquial_claims_train.jsonl','r')asfp:forlineinfp:turns.append(json.loads(line))print(turns[0].keys())# dict_keys(['colloquial_claims', 'fever_claim', 'fever_label', 'evidences', 'gold_evidence_set', 'fever_id'])
In progress