Liar, Liar, Pants on Fire
By G. Korman. 1997
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- James Thorne
- Tal Schuster
- Andreas Vlachos
- Prakhar Gupta
- Neema Kotonya
- Xiang Ren
- Mohit Bansal
- Caiming Xiong
Citations in the corpus
- CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP (2021, background): “…al. 2019, 2020 lama-squad qa/closed-book qa Petroni et al. 2019, 2020 lama-trex qa/closed-book qa Petroni et al. 2019, 2020 liar cls/fact checking Wang 2017 limit other Manotas et al. 2020 math_qa qa/multiple-choice qa Amini et al. 2019 mc_taco qa/binary Zhou et al. 2019 medical_questions_pairs…”
- Explainable Automated Fact-Checking for Public Health Claims (2020, methodology): “We compute the mean and standard deviation for Flesch-Kincaid and Dale-Chall scores of claims for LIAR (Wang, 2017), FEVER (Thorne et al.”
- Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (2021, background): “Most of the fact verification literature focuses on resolving claims to be true or false (Popat et al., 2018; Thorne and Vlachos, 2018; Wang, 2017).”
- Evaluating adversarial attacks against multiple fact verification systems (2019, background): “Advances in this task have been achieved through improved modelling and the availability of resources to train and validate systems (e.g. Wang (2017); Baly et al. (2018b); Thorne et al. (2018)).”
- DialFact: A Benchmark for Fact-Checking in Dialogue (2021, background): “…age to tackle the spread039 of misinformation propagated.040 1Data and code are available at github.com/xyz Fact-checking was introduced in Wang (2017); 041 Thorne et al. (2018) and since then a growing body 042 of research has explored and suggested various 043 tasks and resources to…”