The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Fine-grained Fact Verification with Kernel Graph Attention Network
Open accessAbstract
Fact Verification requires fine-grained natural language inference capability that finds subtle clues to identify the syntactical and semantically correct but not well-supported claims.This paper presents Kernel Graph Attention Network (KGAT), which conducts more finegrained fact verification with kernel-based attentions.Given a claim and a set of potential evidence sentences that form an evidence graph, KGAT introduces node kernels, which better measure the importance of the evidence node, and edge kernels, which conduct fine-grained evidence propagation in the graph, into Graph Attention Networks for more accurate fact verification.KGAT achieves a 70.38%FEVER score and significantly outperforms existing fact verification models on FEVER, a large-scale benchmark for fact verification.Our analyses illustrate that, compared to dot-product attentions, the kernelbased attention concentrates more on relevant evidence sentences and meaningful clues in the evidence graph, which is the main source of KGAT's effectiveness.All source codes of this work are available at https://github. com/thunlp/KernelGAT.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
Deep 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
FEVER: a Large-scale Dataset for Fact Extraction and VERification
Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (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/n18-1074 · Cited by 1,343
The Fact Extraction and VERification (FEVER) Shared Task
Thorne, J., Vlachos, A., Cocarascu, O., Christodoulopoulos, C., & Mittal, A. (2018) · Proceedings of the First Workshop on Fact Extraction and VERification (FEVER) · doi:10.18653/v1/w18-5501 · Cited by 212
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
Nie, Y., Wang, S., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1258 · Cited by 132
UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)
Yoneda, T., Mitchell, J., Welbl, J., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the First Workshop on Fact Extraction and VERification (FEVER) · doi:10.18653/v1/w18-5515 · Cited by 101
Cited by
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880
Self-citationA Survey on Automated Fact-Checking
Guo, Z., Schlichtkrull, M., & Vlachos, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00454 · Cited by 500
Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs
Chen, Z., Mao, H., Li, H., Jin, W., Wen, H., Wei, X., Wang, S., Yin, D., Fan, W., Liu, H., et al. (2024) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3655103.3655110 · Cited by 209
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Xiong, W., Li, X. L., Iyer, S., Du, J., Lewis, P., Wang, W. Y., Mehdad, Y., Yih, W.-T., Riedel, S., Kiela, D., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.12756 · Cited by 112
GERE: Generative Evidence Retrieval for Fact Verification
Chen, J., Zhang, R., Guo, J., Fan, Y., & Cheng, X. (2022) · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3477495.3531827 · Cited by 66
Metadata
- Authors
- Liu, Z., Xiong, C., Sun, M., & Liu, Z.
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 7342-7351
- OpenAlex
- W3016482365
- Cited by
- 183
- References
- 9
- FWCI
- 12.20
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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