[1] THORNE J, VLACHOS A. Automated fact checking: Task formulations,methods and future directions[C]//Proceedings of the 27th International Conference on Computational Linguistics, 2018: 3346-3359.
[2] 谢艺菲,卢琪,刘鑫,等. 基于图的多层次注意力事实验证算法[J]. 计算机工程与应用, 2021, 57(10): 146-153.
[3] OSHIKAWA R, QIAN J, WANG W Y. A survey on natural language processing for fake news detection[C]//Proceedings of the 12th Language Resources and Evaluation Conference, 2020: 6086-6093.
[4] CHEN J, BAO Q, SUN C, et al. LOREN: Logic-regularized reasoning for interpretable fact verification[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(10): 10482-10491.
[5] BIAN T, XIAO X, XU T, et al. Rumor detection on social media with bi-directional graph convolutional networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(01): 549-556.
[6] THORNE J, VLACHOS A, CHRISTODOULOPOULOS C, et al. FEVER: A large-scale dataset for fact extraction and verification[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018: 809-819.
[7] BEKOULIS G, PAPAGIANNOPOULOU C, DELIGIANNIS N. A review on fact extraction and verification[J]. ACM Computing Surveys, 2021, 55(1): 1-35.
[8] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000-6010.
[9] YONEDA T, MITCHELL J, WELBL J, et al. Ucl machine reading group: Four factor framework for fact finding (hexaf)[C]//Proceedings of the 1st Workshop on Fact Extraction and VERification, 2018: 97-102.
[10] KRUENGKRAI C, YAMAGISHI J, WANG X. A multi-level attention model for evidence-based fact checking[C]//Proceedings of the Association for Computational Linguistics: ACL-IJCNLP, 2021: 2447-2460.
[11] TYMOSHENKO K, MOSCHITTI A. Strong and light baseline models for fact-checking joint inference[C]//Proceedings of the Association for Computational Linguistics: ACL-IJCNLP, 2021: 4824-4830.
[12] PARK E,LEE J H, HYEON J D, et al. SISER: Semantic-infused selective graph reasoning for fact verification[C]//Proceedings of the 29th International Conference on Computational Linguistics, 2022: 1367-1378.
[13] WANG Y, QIAN S, HU J, et al. Fake news detec-
tion via knowledge-driven multimodal graph convolutional networks[C]//Proceedings of the International Conference on Multimedia Retrieval, 2020: 540-547.
[14] ZHOU J, HAN X, YANG C, et al. GEAR: Graph-based evidence aggregating and reasoning for fact verification[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 892-901.
[15] ZHONG W, XU J, TANG D, et al. Reasoning over semantic-level graph for fact checking[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 6170-6180.
[16] LIU Z, XIONG C, SUN M, et al. Fine-grained fact verification with kernel graph attention network[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 7342-7351.
[17] HANSELOWSKI A, ZHANG H, LI Z, et al. UKP-athene: Multi-sentence textual entailment for claim verification[C]//Proceedings of the 1st Workshop on Fact Extraction and VERification, 2018: 103-108.
[18] CHEN Q, ZHU X, LING Z H, et al. Enhanced LSTM for natural language inference[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 1657-1668.
[19] NIE Y, CHEN H, BANSAL M. Combining fact extraction and verification with neural semantic matching networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(01): 6859-6866.
[20] KENTON J D M W C, TOUTANOVA L K. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of NAACL-HLT, 2019: 4171-4186.
[21] ZHUANG L, WAYNE L, YA S, et al. A robustly optimized BERT pre-training approach with post-training[C]//Proceedings of the 20th Chinese National Conference on Computational Linguistics, 2021: 1218-1227.
[22] WAN H, CHEN H, DU J, et al. A DQN-based approach to finding precise evidences for fact verification[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021: 1030-1039.
[23] YI F, CHRISTOPHER T, REVANTH G R. InfoSurgeon: Cross-mediafine-grained information consistency checking for fake news detection[C]//Proceedings of the Association for Computational Linguistics, 2021: 1683-1698.[24] DUN Y, TU K, CHEN C, et al. Kan: Knowledge-aware attention network for fake news detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(1): 81-89.
[25] SI J, ZHOU D, LI T, et al. Topic-aware evidence reasoning and stance-aware aggregation for fact verification[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021: 1612-1622.
[26] BASTINGS J, TITOV I, AZIZ W, et al. Graph convolutional encoders for syntax-aware neural machine translation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2017: 1957-1967.
[27] WU M, PAN S, ZHU X, et al. Domain-adversarial graph neural networks for text classification[C]//Proceedings of the IEEE International Conference on Data Mining. IEEE, 2019: 648-657.
[28] REIMERS N, GUREVYCH I. Sentence-bert: Sentence embeddings using Siamese bert-networks[J]. arXiv Preprint arXiv:1908.10084, 2019.
[29] CHEN J, BAO Q, SUN C, et al. LOREN: Logic-regularized reasoning for interpretable fact verification[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(10): 10482-10491.