---
title: "Exploring Pre-trained Language Models for Event — Yang 2019"
description: "The paper addresses two weaknesses of ACE-style event extraction."
authors: ["Sen Yang", "Dawei Feng", "Linbo Qiao", "Zhigang Kan", "Dongsheng Li"]
year: 2019
venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P19-1522/"
doi: "10.18653/v1/p19-1522"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yang-2019-exploring-pre-trained-language-models/"
---
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# Exploring Pre-trained Language Models for Event Extraction and Generation

**Authors:** [Sen Yang](https://md.research.elamur.ai/free/en/authors/sen-yang/), Dawei Feng, Linbo Qiao, Zhigang Kan, [Dongsheng Li](https://md.research.elamur.ai/free/en/authors/dongsheng-li/) · **Year:** 2019 · **Venue:** Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P19-1522/) · [PDF](https://aclanthology.org/P19-1522.pdf) · [DOI 10.18653/v1/p19-1522](https://doi.org/10.18653/v1/p19-1522) · [OpenAlex](https://openalex.org/W2952437275)

**Citations:** 348 citations (openalex-snapshot · 2026-10-14) · [106](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses two weaknesses of ACE-style event extraction. First, one argument often fills several roles in the same event; about 10% of ACE2005 events show this "roles overlap" problem. Second, hand-labeled training data is scarce. The authors propose PLMEE, a two-stage extractor built on BERT. A token-level classifier identifies triggers and their event types. The argument extractor marks the trigger through segment ids and attaches a separate pair of start/end binary classifiers for each role. Because each role is predicted independently, overlapping roles are handled naturally. The loss is also weighted by how strongly each role signals its event type. To get more training data, the authors take existing annotated sentences as prototypes and edit them. They swap in similar arguments and let a language model rewrite the surrounding non-argument tokens. A quality score then ranks the new samples so only the best are kept. Under a stricter evaluation than earlier work, the augmented model sets a new state of the art on ACE2005. This shows that pre-trained language models can serve both for extraction and for producing labeled data.

## Key points
- **Model:** PLMEE uses BERT for both the trigger and argument stages. A three-state finite state machine converts the per-role start/end predictions into argument spans.
- **Role-importance weighting:** Re-weighting the loss improves argument F1 by 0.6 points for identification and 0.5 points for classification.
- **Data generation:** The method combines argument replacement with adjunct token rewriting. A quality score Q mixes perplexity and distance through a weight λ.
- **Best settings:** The best results came from rewriting 40% of adjunct tokens (m = 0.4) and adding generated data equal to the dataset size (n = 1.0). That data was picked from a pool four times the size of ACE2005, using λ = 0.5.
- **Results on the ACE2005 test set:** Trigger classification F1 rose from 80.7% to 81.1%, and argument classification F1 from 58.0% to 58.9%. A noted limitation is that rewriting can change sentence meaning, so role labels may no longer match (e.g., Origin and Destination swapped).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

106 papers of our library cite it (of the 140884 whose bibliographies we read); 4 of them have a page here, 102 not yet.

- [New Insights into Cross-Document Event Coreference: Systematic Comparison and a Simplified Approach](https://md.research.elamur.ai/free/en/papers/cremisini-2020-new-insights-into-cross-document/)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://md.research.elamur.ai/free/en/papers/hsu-2022-degree-data-efficient-generation-based/)
- [Event Extraction as Multi-turn Question Answering](https://md.research.elamur.ai/free/en/papers/li-2020-event-extraction-as-multi-turn/)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://md.research.elamur.ai/free/en/papers/lu-2021-text2event-controllable-sequence-structure-generation/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yang2019exploring,
  title = {{Exploring Pre-trained Language Models for Event Extraction and Generation}},
  author = {Yang, Sen and Feng, Dawei and Qiao, Linbo and Kan, Zhigang and Li, Dongsheng},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1522},
  url = {https://doi.org/10.18653/v1/p19-1522}
}
```

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## References

28 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Yubo Chen, Shulin Liu, Xiang Zhang, Kang Liu, and Jun Zhao. 2017. Automatically labeled data generation for large scale event extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 409–419. [find](https://scholar.google.com/scholar?q=Yubo+Chen%2C+Shulin+Liu%2C+Xiang+Zhang%2C+Kang+Liu%2C+and+Jun+Zhao%2E+2017%2E+Automatically+labeled+data+generation+for+large+scale+event+extraction%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+f)
2. Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 2015. Event extraction via dynamic multi-pooling convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Proces… [find](https://scholar.google.com/scholar?q=Yubo+Chen%2C+Liheng+Xu%2C+Kang+Liu%2C+Daojian+Zeng%2C+and+Jun+Zhao%2E+2015%2E+Event+extraction+via+dynamic+multi%2Dpooling+convolutional+neural+networks%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association) [our card](https://md.research.elamur.ai/free/en/papers/chen-2015-event-extraction-dynamic-multi-pooling/)
3. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. [find](https://scholar.google.com/scholar?q=Jacob+Devlin%2C+Ming%2DWei+Chang%2C+Kenton+Lee%2C+and+Kristina+Toutanova%2E+2018%2E+Bert%3A+Pre%2Dtraining+of+deep+bidirectional+transformers+for+language+understanding%2E+arXiv+preprint+arXiv%3A1810%2E04805%2E)
4. George R Doddington, Alexis Mitchell, Mark A Przybocki, Lance A Ramshaw, Stephanie M Strassel, and Ralph M Weischedel. 2004. The automatic content extraction (ace) program-tasks, data, and evaluation. In LREC, volume 2, page 1. [find](https://scholar.google.com/scholar?q=George+R+Doddington%2C+Alexis+Mitchell%2C+Mark+A+Przybocki%2C+Lance+A+Ramshaw%2C+Stephanie+M+Strassel%2C+and+Ralph+M+Weischedel%2E+2004%2E+The+automatic+content+extraction+%28ace%29+program%2Dtasks%2C+data%2C+and+evaluation%2E) [our card](https://md.research.elamur.ai/free/en/papers/doddington-2004-automatic-content-extraction-ace-program/)
5. Xiaocheng Feng, Lifu Huang, Duyu Tang, Heng Ji, Bing Qin, and Ting Liu. 2016. A language-independent neural network for event detection. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), volume 2, pages 66–71. [find](https://scholar.google.com/scholar?q=Xiaocheng+Feng%2C+Lifu+Huang%2C+Duyu+Tang%2C+Heng+Ji%2C+Bing+Qin%2C+and+Ting+Liu%2E+2016%2E+A+language%2Dindependent+neural+network+for+event+detection%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+fo) [our card](https://md.research.elamur.ai/free/en/papers/feng-2016-language-independent-neural-network-event/)
6. Kelvin Guu, Tatsunori B Hashimoto, Yonatan Oren, and Percy Liang. 2018. Generating sentences by editing prototypes. Transactions of the Association of Computational Linguistics, 6:437–450. [find](https://scholar.google.com/scholar?q=Kelvin+Guu%2C+Tatsunori+B+Hashimoto%2C+Yonatan+Oren%2C+and+Percy+Liang%2E+2018%2E+Generating+sentences+by+editing+prototypes%2E+Transactions+of+the+Association+of+Computational+Linguistics%2C+6%3A437%E2%80%93450%2E) [our card](https://md.research.elamur.ai/free/en/papers/guu-2017-generating-sentences-editing-prototypes/)
7. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770– 778. [find](https://scholar.google.com/scholar?q=Kaiming+He%2C+Xiangyu+Zhang%2C+Shaoqing+Ren%2C+and+Jian+Sun%2E+2016%2E+Deep+residual+learning+for+image+recognition%2E+In+Proceedings+of+the+IEEE+conference+on+computer+vision+and+pattern+recognition%2C+pages+770%E2%80%93+)
8. Yu Hong, Jianfeng Zhang, Bin Ma, Jianmin Yao, Guodong Zhou, and Qiaoming Zhu. 2011. Using cross-entity inference to improve event extraction. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pages 1127– 1136. Associatio… [find](https://scholar.google.com/scholar?q=Yu+Hong%2C+Jianfeng+Zhang%2C+Bin+Ma%2C+Jianmin+Yao%2C+Guodong+Zhou%2C+and+Qiaoming+Zhu%2E+2011%2E+Using+cross%2Dentity+inference+to+improve+event+extraction%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Associati) [our card](https://md.research.elamur.ai/free/en/papers/hong-2011-cross-entity-inference-improve-event/)
9. Jeremy Howard and Sebastian Ruder. 2018. Universal language model fine-tuning for text classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 328–339. [find](https://scholar.google.com/scholar?q=Jeremy+Howard+and+Sebastian+Ruder%2E+2018%2E+Universal+language+model+fine%2Dtuning+for+text+classification%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volum)
10. Lifu Huang, Taylor Cassidy, Xiaocheng Feng, Heng Ji, Clare R Voss, Jiawei Han, and Avirup Sil. 2016. Liberal event extraction and event schema induction. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 258–268. [find](https://scholar.google.com/scholar?q=Lifu+Huang%2C+Taylor+Cassidy%2C+Xiaocheng+Feng%2C+Heng+Ji%2C+Clare+R+Voss%2C+Jiawei+Han%2C+and+Avirup+Sil%2E+2016%2E+Liberal+event+extraction+and+event+schema+induction%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+t)
11. Peifeng Li, Qiaoming Zhu, and Guodong Zhou. 2013a. Joint modeling of argument identification and role determination in chinese event extraction with discourse-level information. In Twenty-Third International Joint Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Peifeng+Li%2C+Qiaoming+Zhu%2C+and+Guodong+Zhou%2E+2013a%2E+Joint+modeling+of+argument+identification+and+role+determination+in+chinese+event+extraction+with+discourse%2Dlevel+information%2E+In+Twenty%2DThird+Intern)
12. Qi Li, Heng Ji, and Liang Huang. 2013b. Joint event extraction via structured prediction with global features. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 73–82. [find](https://scholar.google.com/scholar?q=Qi+Li%2C+Heng+Ji%2C+and+Liang+Huang%2E+2013b%2E+Joint+event+extraction+via+structured+prediction+with+global+features%2E+In+Proceedings+of+the+51st+Annual+Meeting+of+the+Association+for+Computational+Linguistic) [our card](https://md.research.elamur.ai/free/en/papers/li-2013-joint-event-extraction-structured-prediction/)
13. Shasha Liao and Ralph Grishman. 2010. Using document level cross-event inference to improve event extraction. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 789–797. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Shasha+Liao+and+Ralph+Grishman%2E+2010%2E+Using+document+level+cross%2Devent+inference+to+improve+event+extraction%2E+In+Proceedings+of+the+48th+Annual+Meeting+of+the+Association+for+Computational+Linguistics) [our card](https://md.research.elamur.ai/free/en/papers/liao-2010-document-level-cross-event-inference/)
14. Bing Liu, Longhua Qian, Hongling Wang, and Guodong Zhou. 2010. Dependency-driven feature-based learning for extracting protein-protein interactions from biomedical text. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pages 757–765. Association for Computat… [find](https://scholar.google.com/scholar?q=Bing+Liu%2C+Longhua+Qian%2C+Hongling+Wang%2C+and+Guodong+Zhou%2E+2010%2E+Dependency%2Ddriven+feature%2Dbased+learning+for+extracting+protein%2Dprotein+interactions+from+biomedical+text%2E+In+Proceedings+of+the+23rd+Int)
15. Shulin Liu, Yubo Chen, Shizhu He, Kang Liu, and Jun Zhao. 2016. Leveraging framenet to improve automatic event detection. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 2134–2143. [find](https://scholar.google.com/scholar?q=Shulin+Liu%2C+Yubo+Chen%2C+Shizhu+He%2C+Kang+Liu%2C+and+Jun+Zhao%2E+2016%2E+Leveraging+framenet+to+improve+automatic+event+detection%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Computational) [our card](https://md.research.elamur.ai/free/en/papers/liu-2016-leveraging-framenet-improve-automatic-event/)
16. Shulin Liu, Yubo Chen, Kang Liu, and Jun Zhao. 2017. Exploiting argument information to improve event detection via supervised attention mechanisms. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 1789–1798. [find](https://scholar.google.com/scholar?q=Shulin+Liu%2C+Yubo+Chen%2C+Kang+Liu%2C+and+Jun+Zhao%2E+2017%2E+Exploiting+argument+information+to+improve+event+detection+via+supervised+attention+mechanisms%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+As) [our card](https://md.research.elamur.ai/free/en/papers/liu-2017-exploiting-argument-information-improve-event/)
17. Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017. Learned in translation: Contextualized word vectors. In Advances in Neural Information Processing Systems, pages 6294–6305. [find](https://scholar.google.com/scholar?q=Bryan+McCann%2C+James+Bradbury%2C+Caiming+Xiong%2C+and+Richard+Socher%2E+2017%2E+Learned+in+translation%3A+Contextualized+word+vectors%2E+In+Advances+in+Neural+Information+Processing+Systems%2C+pages+6294%E2%80%936305%2E) [our card](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
18. Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2… [find](https://scholar.google.com/scholar?q=Mike+Mintz%2C+Steven+Bills%2C+Rion+Snow%2C+and+Dan+Jurafsky%2E+2009%2E+Distant+supervision+for+relation+extraction+without+labeled+data%2E+In+Proceedings+of+the+Joint+Conference+of+the+47th+Annual+Meeting+of+the+)
19. Makoto Miwa, Rune Sætre, Yusuke Miyao, and Jun’ichi Tsujii. 2009. A rich feature vector for protein-protein interaction extraction from multiple corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1, pages 121–130. Association for Compu… [find](https://scholar.google.com/scholar?q=Makoto+Miwa%2C+Rune+S%C3%A6tre%2C+Yusuke+Miyao%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2009%2E+A+rich+feature+vector+for+protein%2Dprotein+interaction+extraction+from+multiple+corpora%2E+In+Proceedings+of+the+2009+Conference+on+Empir)
20. Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grishman. 2016. Joint event extraction via recurrent neural networks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 300–309. [find](https://scholar.google.com/scholar?q=Thien+Huu+Nguyen%2C+Kyunghyun+Cho%2C+and+Ralph+Grishman%2E+2016%2E+Joint+event+extraction+via+recurrent+neural+networks%2E+In+Proceedings+of+the+2016+Conference+of+the+North+American+Chapter+of+the+Association+) [our card](https://md.research.elamur.ai/free/en/papers/nguyen-2016-joint-event-extraction-recurrent-neural/)
21. Thien Huu Nguyen and Ralph Grishman. 2015. Event detection and domain adaptation with convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short P… [find](https://scholar.google.com/scholar?q=Thien+Huu+Nguyen+and+Ralph+Grishman%2E+2015%2E+Event+detection+and+domain+adaptation+with+convolutional+neural+networks%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+Computational+Ling) [our card](https://md.research.elamur.ai/free/en/papers/nguyen-2015-event-detection-domain-adaptation-convolutional/)
22. Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolog… [find](https://scholar.google.com/scholar?q=Matthew+Peters%2C+Mark+Neumann%2C+Mohit+Iyyer%2C+Matt+Gardner%2C+Christopher+Clark%2C+Kenton+Lee%2C+and+Luke+Zettlemoyer%2E+2018%2E+Deep+contextualized+word+representations%2E+In+Proceedings+of+the+2018+Conference+of+t)
23. Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. URL [https://s3-us-west-2.amazonaws](https://s3-us-west-2.amazonaws). com/openai-assets/research-covers/languageunsupervised/language understanding paper. pdf. [↗](https://s3-us-west-2.amazonaws)
24. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383–2392. [find](https://scholar.google.com/scholar?q=Pranav+Rajpurkar%2C+Jian+Zhang%2C+Konstantin+Lopyrev%2C+and+Percy+Liang%2E+2016%2E+Squad%3A+100%2C000%2B+questions+for+machine+comprehension+of+text%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Methods+in+Natu) [our card](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/)
25. Wilson L Taylor. 1953. “cloze procedure”: A new tool for measuring readability. Journalism Bulletin, 30(4):415–433. [find](https://scholar.google.com/scholar?q=Wilson+L+Taylor%2E+1953%2E+%E2%80%9Ccloze+procedure%E2%80%9D%3A+A+new+tool+for+measuring+readability%2E+Journalism+Bulletin%2C+30%284%29%3A415%E2%80%93433%2E)
26. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144. [find](https://scholar.google.com/scholar?q=Yonghui+Wu%2C+Mike+Schuster%2C+Zhifeng+Chen%2C+Quoc+V+Le%2C+Mohammad+Norouzi%2C+Wolfgang+Macherey%2C+Maxim+Krikun%2C+Yuan+Cao%2C+Qin+Gao%2C+Klaus+Macherey%2C+et+al%2E+2016%2E+Google%E2%80%99s+neural+machine+translation+system%3A+Bridg)
27. Hang Yang, Yubo Chen, Kang Liu, Yang Xiao, and Jun Zhao. 2018. Dcfee: A document-level chinese financial event extraction system based on automatically labeled training data. Proceedings of ACL 2018, System Demonstrations, pages 50–55. [find](https://scholar.google.com/scholar?q=Hang+Yang%2C+Yubo+Chen%2C+Kang+Liu%2C+Yang+Xiao%2C+and+Jun+Zhao%2E+2018%2E+Dcfee%3A+A+document%2Dlevel+chinese+financial+event+extraction+system+based+on+automatically+labeled+training+data%2E+Proceedings+of+ACL+2018%2C+)
28. Ying Zeng, Yansong Feng, Rong Ma, Zheng Wang, Rui Yan, Chongde Shi, and Dongyan Zhao. 2018. Scale up event extraction learning via automatic training data generation. In Thirty-Second AAAI Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Ying+Zeng%2C+Yansong+Feng%2C+Rong+Ma%2C+Zheng+Wang%2C+Rui+Yan%2C+Chongde+Shi%2C+and+Dongyan+Zhao%2E+2018%2E+Scale+up+event+extraction+learning+via+automatic+training+data+generation%2E+In+Thirty%2DSecond+AAAI+Conference+)