The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The Fact Extraction and VERification (FEVER) Shared Task
Open accessAbstract
We present the results of the first Fact Extraction and VERification (FEVER) Shared Task.The task challenged participants to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia.We received entries from 23 competing teams, 19 of which scored higher than the previously published baseline.The best performing system achieved a FEVER score of 64.21%.In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Existing fact-checking approaches either require access to the output probability distribution (which may not be available for systems such as ChatGPT) or external databases that are interfaced via separate, often complex, modules.”
From SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023) · cites this paper as Thorne et al., 2018
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“An alternate approach is to leverage fact-verification approaches, where evidence is retrieved from an external database to assess the veracity of a claim (Thorne et al., 2018; Guo et al., 2022). However, facts can only be assessed relative to the knowledge present in the database.”
From SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023) · cites this paper as Thorne et al., 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015 · cited 3×
“, 2018) and WordNet (Miller, 1995) and Ohio State report using vector representations of named entities.”
BackgroundMethodDeep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
A Decomposable Attention Model for Natural Language Inference
Parikh, A. P., Täckström, O., Das, D., & Uszkoreit, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1244 · Cited by 1,351
Extended Boolean information retrieval
Salton, G., Fox, E. A., & Wu, H. (1983) · Communications of the ACM · doi:10.1145/182.358466 · Cited by 1,058
SciTaiL: A Textual Entailment Dataset from Science Question Answering
Khot, T., Sabharwal, A., & Clark, P. E. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12022 · Cited by 419
Recognizing textual entailment: Rational, evaluation and approaches
Dagan, I., Dolan, B., Magnini, B., & Roth, D. (2009) · Natural Language Engineering · doi:10.1017/s1351324909990209 · Cited by 194
Flagged first, then by citations
Research works printed but not linked: 12
- Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2017. A simple but tough to beat baseline for sentence em- beddings. In Iclr, pages 1-14.
- Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large anno- tated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.
- Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading Wikipedia to Answer Open- Domain Questions. Proc. of ACL'17.
- Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2016. Enhanced LSTM for Natural Language Inference. In Proceedings of the 55th Annual Meeting of the Association for Compu- tational Linguistics, pages 1657-1668, Vancouver, Canada.
- Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, and Antoine Bordes. 2017. Supervised Learning of Universal Sentence Representations from Natural Language Inference Data. In Proceed- ings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 670-680, Copenhagen, Denmark.
- Matt J Kusner, Yu Sun, Nicholas I Kolkin, and Kilian Q Weinberger. 2015. From Word Embeddings To Doc- ument Distances. Proceedings of The 32nd Inter- national Conference on Machine Learning, 37:957- 966.
- Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word rep- resentations. In Proceedings of NAACL-HLT, pages 2227-2237, New Orleans, Louisiana.
- Alec Radford and Tim Salimans. 2018. Improv- ing Language Understanding by Generative Pre- Training. arXiv, pages 1-12.
- Vladimir Svetnik, Andy Liaw, Christopher Tong, J Christopher Culberson, Robert P Sheridan, and Bradley P Feuston. 2003. Random forest: a clas- sification and regression tool for compound classi- fication and qsar modeling. Journal of chemical in- formation and computer sciences, 43(6):1947-1958.
- James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. FEVER: a large-scale dataset for fact extraction and verification. In NAACL-HLT.
- Yi Yang and Ming-Wei Chang. 2015. S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking. In Proceedings of the 53rd Annual Meeting of the Association for Compu- tational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 504-513, Beijing, China.
- Wenpeng Yin, Hinrich Schütze, and Dan Roth. 2018. End-Task Oriented Textual Entailment via Deep Ex- plorations of Inter-Sentence Interactions. In Pro- ceedings of the 56th Annual Meeting of the Associ- ation for Computational Linguistics (Short Papers), pages 540-545, Melbourne, Australia.
Cited by
Sorted by citations
Items 101–200 of 219
Bagla, P., & Kumar, K. (2023) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.316972 · Cited by 9
ProVe: A pipeline for automated provenance verification of knowledge graphs against textual sources
Amaral, G., Rodrigues, O., & Simperl, E. P. B. (2023) · Semantic Web · doi:10.3233/sw-233467 · Cited by 12
Detecting Misinformation on Social Media using Community Insights and Contrastive Learning
Ozcelik, O., Toraman, Ç., & Can, F. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3709009 · Cited by 10
AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media
Shahbazi, Z., Jalali, R., & Shahbazi, Z. (2025) · Future Internet · doi:10.3390/fi17060231 · Cited by 9
Huang, Y.-H., Liu, T., Lee, S.-R., Alvarado, F. H. C., & Chen, Y.-S. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380158 · Cited by 9
FactAlign: Fact-Level Hallucination Detection and Classification Through Knowledge Graph Alignment
Rashad, M., Zahran, A., Amin, A., Abdelaal, A., & Altantawy, M. (2024) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2024.trustnlp-1.8 · Cited by 11
SciClaimHunt: A Large Dataset for Evidence-based Scientific Claim Verification
Kumar, S., Sharma, A., Khincha, S. H., Shroff, G., Singh, S. R., & Mishra, R. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227296 · Cited by 15
Robust Reasoning Over Heterogeneous Textual Information for Fact Verification
Wang, Y., Xia, C., Si, C., Yao, B., & Wang, T. (2020) · IEEE Access · doi:10.1109/access.2020.3019586 · Cited by 9
Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
Jeong, S., Baek, J., Park, C., & Park, J. C. (2021) · Workshop on Scholarly Document Processing (SDP) · doi:10.18653/v1/2021.sdp-1.2 · Cited by 13
Multi-Evidence Based Fact Verification via A Confidential Graph Neural Network
Lan, Y., Liu, Z., Gu, Y., Yi, X., Li, X., Yang, L., & Yu, G. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3403382 · Cited by 8
Verifying the robustness of automatic credibility assessment
Przybyła, P., Shvets, A., & Saggion, H. (2024) · Natural language processing. · doi:10.1017/nlp.2024.54 · Cited by 10
Automated monitoring of online news accuracy with change classification models
Timmerman, Y., & Bronselaer, A. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103105 · Cited by 10
The Choice of Textual Knowledge Base in Automated Claim Checking
Stammbach, D., Zhang, B., & Ash, E. (2023) · Journal of Data and Information Quality · doi:10.1145/3561389 · Cited by 8
Checkworthiness in Automatic Claim Detection Models: Definitions and Analysis of Datasets
Allein, L., & Moens, M.-F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61841-4_1 · Cited by 8
Fake News Detection Techniques for Social Media
Saxena, A., Saxena, P., & Reddy, H. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-3398-0_15 · Cited by 8
Li, X., Wang, W., Fang, J., Jin, L., Kang, H., & Liu, C. (2022) · Applied Sciences · doi:10.3390/app12199688 · Cited by 7
Pipeline and dataset generation for automated fact-checking in almost any language
Drchal, J., Ullrich, H., Mlynář, T., & Moravec, V. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10113-5 · Cited by 8
Transformer-Based Tool for Automated Fact-Checking of Online Health Information: Development Study
Bayani, A., Ayotte, A., & Nikiema, J. N. (2024) · JMIR Infodemiology · doi:10.2196/56831 · Cited by 11
LLMs as Debate Partners: Utilizing Genetic Algorithms and Adversarial Search for Adaptive Arguments
Aryan, P. (2024) · IEEE Conference on Engineering Informatics (ICEI) · doi:10.1109/icei64305.2024.10912343 · Cited by 12
AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification
Tokala, S., Vishal, G. P., Saha, A., & Ganguly, N. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1230 · Cited by 7
Calibration of Machine Reading Systems at Scale
Dhuliawala, S., Adolphs, L., Das, R. K., & Sachan, M. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.133 · Cited by 9
Lee, H., Yoo, K. M., Park, J., Lee, H., & Jung, K. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.76 · Cited by 9
A Review of Techniques to Combat The Peril of Fake News
Rana, D. P., Agarwal, I. Y., & More, A. S. (2018) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2018.8777676 · Cited by 7
Joint Rumour Stance and Veracity
Lillie, A. E., Middelboe, E. R., & Derczynski, L. (2019) · Conference for Truth and Trust Online (TTO) · doi:10.36370/tto.2019.17 · Cited by 7
Natural Language Processing Applications: A New Taxonomy using Textual Entailment
Elshazly, M., Haggag, M. H., & Ehssan, S. A. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120580 · Cited by 7
Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines
Qaiser, A., Hina, S., Kazi, A. K., Ahmed, S., & Asif, R. (2023) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.036784 · Cited by 7
CosG: A Graph-Based Contrastive Learning Method for Fact Verification
Chen, C., Zheng, J., & Chen, H. (2021) · Sensors · doi:10.3390/s21103471 · Cited by 7
Strong and Light Baseline Models for Fact-Checking Joint Inference
Tymoshenko, K., & Moschitti, A. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.426 · Cited by 6
Barrón‐Cedeño, A., Alam, F., Struß, J. M., Nakov, P., Chakraborty, T., Elsayed, T., Przybyła, P., Caselli, T., Martino, G. D. S., Haouari, F., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71908-0_2 · Cited by 6
Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era
Nakov, P., & Martino, G. D. S. (2020) · doi:10.18653/v1/2020.emnlp-tutorials.2 · Cited by 7
Countering Disinformation by Finding Reliable Sources: a Citation-Based Approach
Przybyła, P., Borkowski, P., & Kaczyński, K. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891941 · Cited by 6
CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion
He, X., Gong, Y., Jin, A.-L., Zhang, H., Dong, A., Jiao, J., Yiu, S., & Duan, N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.651 · Cited by 7
RoBERTaEns: Deep Bidirectional Encoder Ensemble Model for Fact Verification
Naseer, M., Windiatmaja, J. H., Asvial, M., & Sari, R. F. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6020033 · Cited by 6
SimpleLSTM: A Deep-Learning Approach to Simple-Claims Classification
Chawla, P., Esteves, D., Pujar, K., & Lehmann, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30244-3_21 · Cited by 5
Bai, Y., Colas, A., & Wang, D. Z. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591907 · Cited by 5
Input-oriented demonstration learning for hybrid evidence fact verification
Chen, C., Chen, W., Zheng, J., Luo, A., Cai, F., & Zhang, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123191 · Cited by 5
Leveraging Graph Structures to Detect Hallucinations in Large Language Models
Nonkes, N., Agaronian, S., Kanoulas, E., & Petcu, R. (2024) · Workshop on Graph Based Methods for Natural Language Processing · doi:10.18653/v1/2024.textgraphs-1.7 · Cited by 8
CFEVER: A Chinese Fact Extraction and VERification Dataset
Lin, Y.-J., Lin, C.-Y., Yeh, C.-J., Li, Y., Hu, Y.-Y., Hsu, C.-H., Lee, M.-F., & Kao, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29825 · Cited by 6
Constrained Fact Verification for FEVER
Pratapa, A., Jayanthi, S. M., & Nerella, K. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.629 · Cited by 5
HOAXPEDIA: A Unified Wikipedia Hoax Articles Dataset
Borkakoty, H., & Espinosa-Anke, L. (2024) · doi:10.18653/v1/2024.wikinlp-1.11 · Cited by 4
Data and Model Distillation as a Solution for Domain-transferable Fact Verification
Mithun, M. P., Suntwal, S., & Surdeanu, M. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.360 · Cited by 4
A simple yet effective self-debiasing framework for transformer models
Wang, X., Liu, X., Wang, L., Wu, S., Su, J., & Wu, H. R. (2024) · Artificial Intelligence · doi:10.1016/j.artint.2024.104258 · Cited by 5
Avinesh, P. V. S., Peyrard, M., & Meyer, C. M. (2021) · Language Resources and Evaluation · doi:10.1007/s10579-020-09513-5 · Cited by 4
Improving Bot Response Contradiction Detection via Utterance Rewriting
Jin, D., Liu, S., Liu, Y., & Hakkani-Tür, D. Z. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.56 · Cited by 4
SciClaimHunt: A Large Dataset for Evidence-based Scientific Claim Verification
Kumar, S., Sharma, A., Khincha, S. H., Shroff, G., Singh, S. R., & Mishra, R. (2025) · arXiv · doi:10.48550/arxiv.2502.10003 · Cited by 3
Panagoulias, D. P., Virvou, M. K., & Tsihrintzis, G. A. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.224 · Cited by 3
Vallayil, M., Nand, P., Yan, W., Allende‐Cid, H., & Vamathevan, T. (2025) · Applied Sciences · doi:10.3390/app15041970 · Cited by 3
Santra, P., Ghosh, M., Ganguly, D., Basuchowdhuri, P., & Naskar, S. K. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70021 · Cited by 5
Heterogeneous-Graph Reasoning and Fine-Grained Aggregation for Fact Checking
Lin, H., & Fu, X. (2022) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2022.fever-1.2 · Cited by 3
Stammbach, D., Varanasi, S., & Neumann, G. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2201 · Cited by 3
Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims?
Wawer, A., Wojdyga, G., & Sarzyńska‐Wawer, J. (2019) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/d19-6602 · Cited by 3
Diaz, G. O., & Ng, V. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i09.7080 · Cited by 3
Roberts, D. A. O. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72240-1_36 · Cited by 4
KaushikAcharya at SemEval-2021 Task 9: Candidate Generation for Fact Verification over Tables
Acharya, K. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.181 · Cited by 3
Towards universal methods for fake news detection
Pszona, M., Janicka, M., Wojdyga, G., & Wawer, A. (2022) · Natural Language Engineering · doi:10.1017/s1351324922000456 · Cited by 5
Enhancing Information Retrieval in Fact Extraction and Verification
Olivares, D. G., Quijano-Sánchez, L., & Liberatore, F. (2023) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2023.fever-1.4 · Cited by 4
Claim Verification using a Multi-GAN based Model
Hatua, A., Mukherjee, A., & Verma, R. M. (2021) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-072-4_056 · Cited by 3
A Comparative Study of Ensemble Approaches to Fact-Checking for the FEVER Shared Task
Oni, O. O., & van Zyl, T. L. (2020) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde50874.2020.9411564 · Cited by 3
Fact Extraction and VERification -- The FEVER case: An OverviewPreprint
Bekoulis, G., Papagiannopoulou, C., & Deligiannis, N. (2020) · arXiv (Cornell University) · Cited by 3
WikiCheck: An end-to-end open source Automatic Fact-Checking API based on WikipediaPreprint
Trokhymovych, M., & Sáez-Trumper, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.00835 · Cited by 3
Advancing Vietnamese Fact Extraction and Verification through Multi-Stage Text Ranking
Tran, Q.-D., Tran, T.-H., & Tran, K. Q. (2024) · International Conference on Multimedia Analysis and Pattern Recognition (MAPR) · doi:10.1109/mapr63514.2024.10660734 · Cited by 2
HF-RAG: Hierarchical Fusion-based RAG with Multiple Sources and Rankers
Santra, P., Ghosh, M., Ganguly, D., Basuchowdhuri, P., & Naskar, S. K. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760942 · Cited by 4
Multi-agent systems and credibility-based advanced scoring mechanism in fact-checking
Yihan, D. O. N. G., & Itō, T. (2026) · Scientific Reports · doi:10.1038/s41598-026-41862-z · Cited by 2
A Hybrid Lexical-Semantic Approach for Stance Detection
Semwal, T., Jain, S., Mandhani, S. P., Semwal, H., Dalvi, A., & Nair, R. (2025) · World Skills Conference on Universal Data Analytics and Sciences (WorldSUAS) · doi:10.1109/worldsuas66815.2025.11199270 · Cited by 2
Multi-Layer Ranking with Large Language Models for News Source Recommendation
Zhang, W., Gui, L., Procter, R., & He, Y. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657966 · Cited by 2
Deng, X., Wang, X., & Stevenson, M. (2025) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3731120.3744614 · Cited by 5
Stahlhut, C. (2019) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/d19-6613 · Cited by 2
Towards Better Evidence Extraction Methods for Fact-Checking Systems
Azevedo, P., Rocha, G., Esteves, D., & Cardoso, H. L. (2021) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3486622.3493930 · Cited by 3
Danish Stance Classification and Rumour ResolutionPreprint
Lillie, A. E., & Middelboe, E. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.01304 · Cited by 2
OpenMatch: An Open-Source Package for Information Retrieval.Preprint
Liu, Z., Zhang, K., Xiong, C., & Liu, Z. (2021) · arXiv (Cornell University) · Cited by 2
Extracting and filtering paraphrases by bridging natural language inference and paraphrasingPreprint
Klemen, M., & Robnik‐Šikonja, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.07119 · Cited by 2
Detecção automática de notícias falsas em português
Santos, R. L. D. S. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2022.tde-14072022-165613 · Cited by 2
Combatting Disinformation via Interactive Evidence Detection
Stahlhut, C. (2019) · Conference for Truth and Trust Online (TTO) · doi:10.36370/tto.2019.9 · Cited by 1
Knowledge-Enhanced Graph Attention Network for Fact Verification
Chen, C., Zheng, J., & Chen, H. (2021) · Mathematics · doi:10.3390/math9161949 · Cited by 2
Artificial Intelligence Fact-checking Technology and the Sociotechnical Definition of ‘Factuality’
Lee, J., & Park, S. (2024) · Korean Journal of Journalism & Communication Studies · doi:10.20879/kjjcs.2024.68.4.004 · Cited by 1
Dynamic Margin-based Contrastive Learning for Robust Negative Sampling in Information Retrieval
Chen, T.-T., Wang, C., & Tsai, M.-F. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730182 · Cited by 1
Levantamento e Análise Qualitativa de Bases de Dados de Fake News em Português
Baracho, J. K. D. C. M., Lisboa, L. A., & Lopes, R. V. V. (2025) · Anais do Workshop sobre as Implicações da Computação na Sociedade (WICS) · doi:10.5753/wics.2025.9464 · Cited by 1
Scudo, F. L. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-31469-8_2 · Cited by 1
Smart Lexical Search for Label Flipping Adversial Attack
Gutiérrez-Megías, A., Jiménez-Zafra, S. M., Ureña, L. A., & Martínez‐Cámara, E. (2024) · Workshop on Privacy in Natural Language Processing (PrivateNLP) · doi:10.18653/v1/2024.privatenlp-1.11 · Cited by 1
AMIF: A Hybrid Model for Improving Fact Checking in Product Question Answering
Lin, H., Chen, L., Ma, J., Yang, Z., & Chen, G. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892491 · Cited by 1
Towards Stable Natural Language Understanding via Information Entropy Guided Debiasing
Du, L., Ding, X. H., Sun, Z., Liu, T., Qin, B., & Liu, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.161 · Cited by 1
CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing
Dou, S., Gao, S., Gui, T., & Zhang, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9431-7_22 · Cited by 1
Meta Answering for Machine ReadingPreprint
Börschinger, B., Boyd-Graber, J. L., Buck, C., Bulian, J., Ciaramita, M., Huebscher, M. C., Gajewski, W., Kilcher, Y., Nogueira, R. F., & Saralegui, L. S. (2019) · arXiv (Cornell University) · doi:10.3929/ethz-b-000393755 · Cited by 1
Coarse-to-Fine Memory Matching for Joint Retrieval and ClassificationPreprint
Schmaltz, A., & Beam, A. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.02287 · Cited by 1
Adapting Deep Neural Information Retrieval Models to Long Documents and New Domains
Li, M. (2023) · theses.fr (ABES) · doi:10.70675/b7c1a41fz0808z4c09zac20z6c9e21586cb0 · Cited by 1
Pretraining Language Models Using Translationese
Doshi, M., Dabre, R., & Bhattacharyya, P. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.334 · Cited by 1
Validation of Facts Against Textual Sources
Pendyala, V. K., Sinha, S., Prakash, S., Reddy, S., & Jamatia, A. (2019) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-056-4_104
DSNDM: Deep Siamese Neural Discourse Model with Attention for Text Pairs Categorization and Ranking
Chernyavskiy, A., & Ilvovsky, D. (2020) · Workshop on Computational Approaches to Discourse (CODI) · doi:10.18653/v1/2020.codi-1.8
Graph Attention and Interaction Network With Multi-Task Learning for Fact Verification
Yang, R., Wang, R., & Ling, Z.-H. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414889
Joint Models for Answer Verification in Question Answering SystemsPreprint
Zhang, Z., Vu, T., & Moschitti, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.04217
Mithun, M. P., Suntwal, S., & Surdeanu, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.558
Correcting Texts Generated by Transformers using Discourse Features andWeb Mining
Chernyavskiy, A., Ilvovsky, D., & Galitsky, B. A. (2021) · Student Research Workshop .../Proceedings of the Student Research Workshop ... · doi:10.26615/issn.2603-2821.2021_006
Image Captioning with External Knowledge
Nikiforova, S. (2023) · Utrecht University Repository (Utrecht University) · doi:10.33540/1709
Robust and Efficient Claim Retrieval for Online Fact-Checking ApplicationsPreprint
Boland, K., Hövelmeyer, A., Fafalios, P., Todorov, K., Mazhar, U., & Dietze, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3007151/v1
Metadata
- Authors
- Thorne, J., Vlachos, A., Cocarascu, O., Christodoulopoulos, C., & Mittal, A.
- Affiliations
- University of Sheffield (GB)
- Imperial College London (GB)
- Amazon (United States) (US)
- Venue
- Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
- Published
- 2018
- Pages
- 1-9
- OpenAlex
- W2902421513
- Cited by
- 212
- References
- 6
- Citations by year
- 2026: 7 · 2025: 19 · 2024: 21 · 2023: 27 · 2022: 32 · 2021: 53 · 2020: 27 · 2019: 25 · 2018: 1
- FWCI
- 10.82
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Fact Extraction and VERification Workshop (FEVER) (publishedVersion, cc-by)
- arXiv (Cornell University) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Horizon 2020 Framework Programme (EU‐H2020)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- fact-checking, fact extraction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
