--- title: "Adversarial Training for Weakly Supervised Event Detection (2019)" description: "Event detection (ED) means finding trigger words and assigning event types, and it usually… 75.1 F1. 3 references, cited by 62 papers here." authors: ["Xiaozhi Wang", "Xu Han", "Zhiyuan Liu", "Maosong Sun", "Peng Li"] year: 2019 venue: "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)" source: "https://aclanthology.org/N19-1105/" doi: "10.18653/v1/n19-1105" 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/wang-2019-adversarial-training-weakly-supervised-event/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2019-adversarial-training-weakly-supervised-event/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2019](https://md.research.elamur.ai/free/en/papers/2019/page/4/) / Adversarial Training for Weakly Supervised Event Detection # Adversarial Training for Weakly Supervised Event Detection **Authors:** [Xiaozhi Wang](https://md.research.elamur.ai/free/en/authors/xiaozhi-wang/), [Xu Han](https://md.research.elamur.ai/free/en/authors/xu-han/), [Zhiyuan Liu](https://md.research.elamur.ai/free/en/authors/zhiyuan-liu/), [Maosong Sun](https://md.research.elamur.ai/free/en/authors/maosong-sun/) and [Peng Li](https://md.research.elamur.ai/free/en/authors/peng-li/) · **Year:** 2019 · **Venue:** [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)](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2019](https://md.research.elamur.ai/free/en/venues/naacl/2019/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/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/N19-1105/) · [PDF](https://aclanthology.org/N19-1105.pdf) · [DOI 10.18653/v1/n19-1105](https://doi.org/10.18653/v1/n19-1105) · [OpenAlex](https://openalex.org/W2946760275) **Citations:** 148 citations (OpenAlex, 2026-10-14) · [62](https://md.research.elamur.ai/free/en/papers/wang-2019-adversarial-training-weakly-supervised-event/cited-by/) in our library, of the 141,840 papers whose bibliographies we read ## Summary Event detection (ED) means finding trigger words and assigning event types, and it usually depends on costly manual annotation. Existing weakly supervised alternatives label data automatically with hand-crafted rules and knowledge bases. The resulting data is noisy, covers few topics, and is skewed toward some of them. Wang et al. address this in two steps. First, a trigger-based latent instance discovery strategy treats every sentence containing a known trigger word as a possible event instance, which yields a broad candidate set without manual rule design. Second, a GAN-style scheme separates reliable from unreliable data. A discriminator learns to recognize correctly labeled, informative instances, and a generator picks the most confusing unreliable instances to mislead it. Once the two reach balance, the discriminator is more robust to noise and the generator surfaces useful new examples. With DMCNN and DMBERT encoders, the method beats prior systems in both distantly supervised and semi-supervised settings. This shows that ED training data can be expanded cheaply while keeping quality high. ## Key points - Encoders are CNN and BERT with dynamic multi-pooling around the candidate trigger, named DMCNN and DMBERT. Code and data are available at github.com/thunlp/Adv-ED. - In the semi-supervised setting, ACE-2005 triggers (599 documents, 8 types, 33 subtypes) seed candidate harvesting from the New York Times corpus. The filtered data then extends the ACE-2005 training set. - On ACE-2005, DMBERT+Boot reaches 75.1 F1 (DMBERT: 74.6), the best among compared systems including GCN-ED (73.1). DMCNN+Boot gains 1.7 points over DMCNN (70.8 vs 69.1). - A manual check had four annotators judge 150 sampled instances. Precision was 91.7% after the first iteration and 87.5% after the second, versus 88.9% for Chen et al. (2017) and 91.0% for Zeng et al. (2018). - In distantly supervised experiments, the adversarial method outperformed all baselines on every reported metric, including +MIL and +NA variants. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,085 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,888 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 1,282 works 🧭 Topics (1): [🔎 Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/) · 109 works ## Cite Cite as: Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun and Peng Li. 2019. Adversarial Training for Weakly Supervised Event Detection. 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). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{wang2019adversarial, title = {{Adversarial Training for Weakly Supervised Event Detection}}, author = {Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun and Peng Li}, booktitle = {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)}, year = {2019}, doi = {10.18653/v1/n19-1105}, url = {https://doi.org/10.18653/v1/n19-1105} } ``` - [← previous Answers Unite! 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[find](https://scholar.google.com/scholar?q=Dell+sued+for+%E2%80%9Dbait+and+switch%E2%80%9D+and+false+promises%2E+Discovered+1%2E+The+lawyers+for+the+four+former+state+officials+who+have+been+sued+told+the+jurors+%2E+%2E+%2E+2%2E+But+litigation+held+up+the+project+until+%2E) 3. David Ahn. 2006. [The stages of event extraction](https://md.research.elamur.ai/free/en/papers/ahn-2006-stages-event-extraction/). In Proceedings of ACL Workshop on Annotating and Reasoning about Time and Events, pages 1–8. *Table 5: The examples with highlighting triggers.* *Updated 24 October 2026*