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In-context Contrastive Learning for Event Causality Identification (2405.10512v2)

Published 17 May 2024 in cs.IR and cs.LG

Abstract: Event Causality Identification (ECI) aims at determining the existence of a causal relation between two events. Although recent prompt learning-based approaches have shown promising improvements on the ECI task, their performance are often subject to the delicate design of multiple prompts and the positive correlations between the main task and derivate tasks. The in-context learning paradigm provides explicit guidance for label prediction in the prompt learning paradigm, alleviating its reliance on complex prompts and derivative tasks. However, it does not distinguish between positive and negative demonstrations for analogy learning. Motivated from such considerations, this paper proposes an In-Context Contrastive Learning (ICCL) model that utilizes contrastive learning to enhance the effectiveness of both positive and negative demonstrations. Additionally, we apply contrastive learning to event pairs to better facilitate event causality identification. Our ICCL is evaluated on the widely used corpora, including the EventStoryLine and Causal-TimeBank, and results show significant performance improvements over the state-of-the-art algorithms.

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References (40)
  1. Personalized public policy analysis in social sciences using causal-graphical normalizing flows. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 11810–11818.
  2. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150.
  3. Modeling biological processes for reading comprehension. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1499–1510.
  4. Manvi Breja and Sanjay Kumar Jain. 2020. Causality for question answering. In COLINS, pages 884–893.
  5. Knowledge-enriched event causality identification via latent structure induction networks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4862–4872.
  6. Tommaso Caselli and Piek Vossen. 2017. The event storyline corpus: A new benchmark for causal and temporal relation extraction. In Proceedings of the Events and Stories in the News Workshop, pages 77–86.
  7. Ergo: Event relational graph transformer for document-level event causality identification. arXiv preprint arXiv:2204.07434.
  8. Enhanced lstm for natural language inference. arXiv preprint arXiv:1609.06038.
  9. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  10. Prompt-learning for fine-grained entity typing. arXiv preprint arXiv:2108.10604.
  11. A survey for in-context learning. arXiv preprint arXiv:2301.00234.
  12. Towards event-level causal relation identification. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1828–1833.
  13. Is chatgpt a good causal reasoner? a comprehensive evaluation. arXiv preprint arXiv:2305.07375.
  14. Modeling document-level causal structures for event causal relation identification. In 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), pages 1808–1817.
  15. Deberta: Decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654.
  16. Geoffrey E Hinton and Sam Roweis. 2002. Stochastic neighbor embedding. Advances in neural information processing systems, 15.
  17. Semantic structure enhanced event causality identification. arXiv preprint arXiv:2305.12792.
  18. Supervised contrastive learning. Advances in neural information processing systems, 33:18661–18673.
  19. What makes good in-context examples for gpt-3333? arXiv preprint arXiv:2101.06804.
  20. Knowledge enhanced event causality identification with mention masking generalizations. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pages 3608–3614.
  21. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1–35.
  22. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
  23. Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101.
  24. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
  25. Paramita Mirza and Sara Tonelli. 2014. An analysis of causality between events and its relation to temporal information. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 2097–2106.
  26. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543.
  27. Minh Tran Phu and Thien Huu Nguyen. 2021. Graph convolutional networks for event causality identification with rich document-level structures. In Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: Human language technologies, pages 3480–3490.
  28. Temporal sentiment analysis and causal rules extraction from tweets for event prediction. Procedia computer science, 48:84–89.
  29. Enhancing event causality identification with event causal label and event pair interaction graph. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10314–10322.
  30. Learning causality for news events prediction. In Proceedings of the 21st international conference on World Wide Web, pages 909–918.
  31. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485–5551.
  32. Event causality identification via derivative prompt joint learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2288–2299.
  33. Conceptnet 5.5: An open multilingual graph of general knowledge. In Proceedings of the AAAI conference on artificial intelligence, volume 31.
  34. Ernie: Enhanced representation through knowledge integration. arXiv preprint arXiv:1904.09223.
  35. Transprompt: Towards an automatic transferable prompting framework for few-shot text classification. In Proceedings of the 2021 conference on empirical methods in natural language processing, pages 2792–2802.
  36. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, pages 38–45.
  37. Connprompt: Connective-cloze prompt learning for implicit discourse relation recognition. In Proceedings of the 29th International Conference on Computational Linguistics, pages 902–911.
  38. Document-level event causality identification via graph inference mechanism. Information Sciences, 561:115–129.
  39. Improving event causality identification via self-supervised representation learning on external causal statement. arXiv preprint arXiv:2106.01654.
  40. Learnda: Learnable knowledge-guided data augmentation for event causality identification. arXiv preprint arXiv:2106.01649.

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