Abstract
This paper focuses on document-level event factuality identification (DEFI), which predicts the factual nature of an event from the view of a document. As the document-level sub-task of event factuality identification (EFI), DEFI is a challenging and fundamental task in natural language processing (NLP). Currently, most existing studies focus on sentence-level event factuality identification (SEFI). However, DEFI is still in the early stage and related studies are quite limited. Previous work is heavily dependent on various NLP tools and annotated information, e.g., dependency trees, event triggers, speculative and negative cues, and does not consider filtering irrelevant and noisy texts that can lead to wrong results. To address these issues, this paper proposes a reinforced multi-granularity hierarchical network model: Reinforced Semantic Learning Network (RSLN), which means it can learn semantics from sentences and tokens at various levels of granularity and hierarchy. Since integrated with hierarchical reinforcement learning (HRL), the RSLN model is able to select relevant and meaningful sentences and tokens. Then, RSLN encodes the event and document according to these selected texts. To evaluate our model, based on the DLEF (Document-Level Event Factuality) corpus, we annotate the ExDLEF corpus as the benchmark dataset. Experimental results show that the RSLN model outperforms several state-of-the-arts.
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Rudinger R, White A S, Van Durme B. Neural models of factuality. In Proc. the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun. 2018, pp.731–744. DOI: https://doi.org/10.18653/v1/n18-1067.
Qian Z, Li P, Zhou G, Zhu Q. Event factuality identification via hybrid neural networks. In Proc. the 25th International Conference on Neural Information Processing, Dec. 2018, pp.335–347. DOI: https://doi.org/10.1007/978-3-030-04221-9_30.
Qian Z, Li P, Zhang Y, Zhou G, Zhu Q. Event factuality identification via generative adversarial networks with auxiliary classification. In Proc. the 27th International Joint Conference on Artificial Intelligence, Jul. 2018, pp.4293–4300. DOI: https://doi.org/10.24963/ijcai.2018/597.
Sheng J, Zou B, Gong Z, Hong Y, Zhou G. Chinese event factuality detection. In Proc. the 8th CCF International Conference on Natural Language Processing and Chinese Computing, Oct. 2019, pp.486–496. DOI: https://doi.org/10.1007/978-3-030-32236-6_44.
Veyseh A P B, Nguyen T H, Dou D. Graph based neural networks for event factuality prediction using syntactic and semantic structures. In Proc. the 57th Conference of the Association for Computational Linguistics, Oct. 2019, pp.4393–4399. DOI: https://doi.org/10.18653/v1/p19-1432.
Qian Z, Li P, Zhu Q, Zhou G. Document-level event factuality identification via adversarial neural network. In Proc. the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun. 2019, pp.2799–2809. DOI: https://doi.org/10.18653/v1/n19-1287.
Huang R, Zou B, Wang H, Li P, Zhou G. Event factuality detection in discourse. In Proc. the 8th CCF International Conference on Natural Language Processing and Chinese Computing, Oct. 2019, pp.404–414. DOI: https://doi.org/10.1007/978-3-030-32236-6_36.
Cao P, Chen Y, Yang Y, Liu K, Zhao J. Uncertain local-to-global networks for document-level event factuality identification. In Proc. the 2021 Conference on Empirical Methods in Natural Language Processing, Nov. 2021, pp.2636–2645. DOI: https://doi.org/10.18653/v1/2021.emnlp-main.207.
Liu J, Pan F, Luo L. GoChat: Goal-oriented chatbots with hierarchical reinforcement learning. In Proc. the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2020, pp.1793–1796. DOI: https://doi.org/10.1145/3397271.3401250.
Wang J, Sun C, Li S, Wang J, Si L, Zhang M, Liu X, Zhou G. Human-like decision making: Document-level aspect sentiment classification via hierarchical reinforcement learning. In Proc. the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Nov. 2019, pp.5580–5589. DOI: https://doi.org/10.18653/v1/D19-1560.
Xiao L, Wang L, He H, Jin Y. Copy or rewrite: Hybrid summarization with hierarchical reinforcement learning. In Proc. the 34th AAAI Conference on Artificial Intelligence, Feb. 2020, pp.9306–9313. DOI: https://doi.org/10.1609/AAAI.V34I05.6470.
Wan G, Pan S, Gong C, Zhou C, Haffari G. Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning. In Proc. the 29th International Joint Conference on Artificial Intelligence, Jul. 2020, pp.1926–1932. DOI: https://doi.org/10.24963/ijcai.2020/267.
Zhou X, Luo S, Wu Y. Co-attention hierarchical network: Generating coherent long distractors for reading comprehension. In Proc. the 34th AAAI Conference on Artificial Intelligence, Feb. 2020, pp.9725–9732. DOI: https://doi.org/10.1609/AAAI.V34I05.6522.
Wu L, Rao Y, Zhao Y, Liang H, Nazir A. DTCA: Decision tree-based co-attention networks for explainable claim verification. In Proc. the 58th Annual Meeting of the Association for Computational Linguistics, Jul. 2020, pp.1024–1035. DOI: https://doi.org/10.18653/v1/2020.acl-main.97.
Wu Y, Zhan P, Zhang Y, Wang L, Xu Z. Multimodal fusion with co-attention networks for fake news detection. In Proc. the 2021 Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Aug. 2021, pp.2560–2569. DOI: https://doi.org/10.18653/v1/2021.findings-acl.226.
Lai T M, Tran Q H, Bui T, Kihara D. A gated self-attention memory network for answer selection. In Proc. the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Nov. 2019, pp.5953–5959. DOI: https://doi.org/10.18653/v1/D19-1610.
Xue L, Li X, Zhang N L. Not all attention is needed: Gated attention network for sequence data. In Proc. the 34th AAAI Conference on Artificial Intelligence, Feb. 2020, pp.6550–6557. DOI: https://doi.org/10.1609/AAAI.V34I04.6129.
Liu L, Chen H, Sun Y. A multi-classification sentiment analysis model of Chinese short text based on gated linear units and attention mechanism. Trans. Asian and Low-Resource Language Information Processing, 2021, 20(6): Article No. 109. DOI: https://doi.org/10.1145/3464425.
Chen Z, Hui S C, Zhuang F, Liao L, Li F, Jia M, Li J. EvidenceNet: Evidence fusion network for fact verification. In Proc. the 2022 ACM Web Conference, Apr. 2022, pp.2636–2645. DOI: https://doi.org/10.1145/3485447.3512135.
Chen J, Bao Q, Sun C, Zhang X, Chen J, Zhou H, Xiao Y, Li L. LOREN: Logic-regularized reasoning for interpretable fact verification. In Proc. the 36th AAAI Conference on Artificial Intelligence, Feb. 2022, pp.10482–10491. DOI: https://doi.org/10.1609/AAAI.V36I10.21291.
Ma J, Gao W, Joty S, Wong K F. Sentence-level evidence embedding for claim verification with hierarchical attention networks. In Proc. the 57th Annual Meeting of the Association for Computational Linguistics, Jul. 2019, pp.2561–2571. DOI: https://doi.org/10.18653/v1/p19-1244.
Chen J, Zhang R, Guo J, Fan Y, Cheng X. GERE: Generative evidence retrieval for fact verification. In Proc. the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2022, pp.2184–2189. DOI: https://doi.org/10.1145/3477495.3531827.
Qian Z, Li P, Zhu Q, Zhou G. Document-level event factuality identification via reinforced multi-granularity hierarchical attention networks. In Proc. the 31st International Joint Conference on Artificial Intelligence, Jul. 2022, pp.4338–4345. DOI: https://doi.org/10.24963/ijcai.2022/602.
Pennington J, Socher R, Manning C D. GloVe: Global vectors for word representation. In Proc. the 2014 Conference on Empirical Methods in Natural Language Processing, Oct. 2014, pp.1532–1543. DOI: https://doi.org/10.3115/v1/d14-1162.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I. Attention is all you need. In Proc. the 31st International Conference on Neural Information Processing Systems, Dec. 2017, pp.5998–6008.
Zhang T, Huang M, Zhao L. Learning structured representation for text classification via reinforcement learning. In Proc. the 32nd AAAI Conference on Artificial Intelligence, Feb. 2018, pp.6053–6060. DOI: https://doi.org/10.1609/AAAI.V32I1.12047.
Williams R J. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 1992, 8(3): 229–256. DOI: https://doi.org/10.1007/BF00992696.
Sutton R S, McAllester D, Singh S, Mansour Y. Policy gradient methods for reinforcement learning with function approximation. In Proc. the 12th International Conference on Neural Information Processing Systems, Nov. 1999, pp.1057–1063.
Kingma D P, Ba J. Adam: A method for stochastic optimization. In Proc. 3rd International Conference on Learning Representations, 2015. DOI: https://doi.org/10.48550/arXiv.1412.6980.
Robbins H, Monro S. A stochastic approximation method. The Annals of Mathematical Statistics, 1951, 22(3): 400–407. DOI: https://doi.org/10.1214/aoms/1177729586.
Zhang H, Qian Z, Zhu X, Li P. Document-level event factuality identification using negation and speculation scope. In Proc. the 28th International Conference on Neural Information Processing, Dec. 2021, pp.414–425. DOI: https://doi.org/10.1007/978-3-030-92185-9_34.
Devlin J, Chang M W, Lee K, Toutanova K. BERT: Pretraining of deep bidirectional transformers for language understanding. In Proc. the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun. 2019, pp.4171–4186. DOI: https://doi.org/10.18653/v1/n19-1423.
Duan J, Zhang Y, Ding X, Chang C Y, Liu T. Learning target-specific representations of financial news documents for cumulative abnormal return prediction. In Proc. the 27th International Conference on Computational Linguistics, Aug. 2018, pp.2823–2833.
Duan J, Ding X, Zhang Y, Liu T. TEND: A target-dependent representation learning framework for news document. IEEE/ACM Trans Audio, Speech, and Language Processing, 2019, 27(12): 2313–2325. DOI https://doi.org/10.1109/TASLP.2019.2947364.
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The work was supported by the National Natural Science Foundation of China under Grant Nos. 62006167, 62276177, 62376181, and 62376178, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 24KJB520036, and the Project Funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.
Zhong Qian received his B.S. and Ph.D. degrees in computer science and technology from Soochow University, Suzhou, in 2012 and 2018, respectively. Currently, he is an associate professor in the School of Computer Science and Technology, Soochow University, Suzhou. His main research interest is information extraction in natural language processing.
Pei-Feng Li received his B.S., M.S., and Ph.D. degrees all in computer science from Soochow University, Suzhou, in 1994, 1997, and 2006, respectively. Currently, he is a professor at the School of Computer Science and Technology, and AI Research Institute, Soochow University, Suzhou. His current research interests include Chinese information processing, machine learning, and information extraction.
Qiao-Ming Zhu received his Ph.D. degree in computer science and technology from Soochow University, Suzhou, in 2008. Currently, he is a professor at the School of Computer Science and Technology, and AI Research Institute, Soochow University, and acts as the director of Department of Science, Technology and Industry in Soochow University, Suzhou. His research interests include natural language processing, information extraction, and embedded systems.
Guo-Dong Zhou received his Ph.D. degree in computer science from the National University of Singapore, Singapore, in 1999. Currently, he is a professor at the School of Computer Science and Technology, and AI Research Institute, Soochow University, Suzhou. His research interests include natural language processing, information extraction, and machine learning.
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Qian, Z., Li, PF., Zhu, QM. et al. Document-Level Event Factuality Identification via Reinforced Semantic Learning Network. J. Comput. Sci. Technol. 39, 1248–1268 (2024). https://doi.org/10.1007/s11390-024-2655-1
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DOI: https://doi.org/10.1007/s11390-024-2655-1