Improving Event Definition Following For Zero-Shot Event Detection

Improving Event Definition Following For Zero-Shot Event Detection

Zefan Cai, Po-Nien Kung, Ashima Suvarna, Mingyu Derek Ma, Hritik Bansal, Baobao Chang, P. Jeffrey Brantingham, Wei Wang, and Nanyun Peng, in Proceedings of The 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024.

Abstract

Bib Entry

@inproceedings{cai2024improving,
  title = {Improving Event Definition Following For Zero-Shot Event Detection},
  author = {Cai, Zefan and Kung, Po-Nien and Suvarna, Ashima and Ma, Mingyu Derek and Bansal, Hritik and Chang, Baobao and Brantingham, P. Jeffrey and Wang, Wei and Peng, Nanyun},
  booktitle = {Proceedings of The 62nd Annual Meeting of the Association for Computational Linguistics (ACL)},
  year = {2024}
}

Related Publications

1. Improving Event Definition Following For Zero-Shot Event Detection

   Zefan Cai, Po-Nien Kung, Ashima Suvarna, Mingyu Derek Ma, Hritik Bansal, Baobao Chang, P. Jeffrey Brantingham, Wei Wang, and Nanyun Peng, in Proceedings of The 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
   BibTeX Details

   @inproceedings{cai2024improving,
     title = {Improving Event Definition Following For Zero-Shot Event Detection},
     author = {Cai, Zefan and Kung, Po-Nien and Suvarna, Ashima and Ma, Mingyu Derek and Bansal, Hritik and Chang, Baobao and Brantingham, P. Jeffrey and Wang, Wei and Peng, Nanyun},
     booktitle = {Proceedings of The 62nd Annual Meeting of the Association for Computational Linguistics (ACL)},
     year = {2024}
   }
   

   Details

2. Argument-Aware Approach To Event Linking

   I.-Hung Hsu, Zihan Xue, Nilay Pochhi, Sahil Bansal, Prem Natarajan, Jayanth Srinivasa, and Nanyun Peng, in Findings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL-Findings), 2024.
   BibTeX Details

   @inproceedings{hsu2024evelink,
     title = {Argument-Aware Approach To Event Linking},
     author = {Hsu, I-Hung and Xue, Zihan and Pochhi, Nilay and Bansal, Sahil and Natarajan, Prem and Srinivasa, Jayanth and Peng, Nanyun},
     booktitle = {Findings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL-Findings)},
     year = {2024}
   }
   

   Details

3. TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction

   Kuan-Hao Huang, I.-Hung Hsu, Tanmay Parekh, Zhiyu Xie, Zixuan Zhang, Prem Natarajan, Kai-Wei Chang, Nanyun Peng, and Heng Ji, in Findings of the Association for Computational Linguistics: ACL (ACL-findings), 2024.
   BibTeX Details

   @inproceedings{Huang2024,
     title = {TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction},
     author = {Huang, Kuan-Hao and Hsu, I-Hung and Parekh, Tanmay and Xie, Zhiyu and Zhang, Zixuan and Natarajan, Prem and Chang, Kai-Wei and Peng, Nanyun and Ji, Heng},
     booktitle = {Findings of the Association for Computational Linguistics: ACL (ACL-findings)},
     year = {2024}
   }
   

   Details

4. Contextual Label Projection for Cross-Lingual Structured Prediction

   Tanmay Parekh, I.-Hung Hsu, Kuan-Hao Huang, Kai-Wei Chang, and Nanyun Peng, in Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024.
   Full Text Code BibTeX Details 🏆 Best Paper Nomination

   @inproceedings{parekh2024clap,
     title = {Contextual Label Projection for Cross-Lingual Structured Prediction},
     author = {Parekh, Tanmay and Hsu, I-Hung and Huang, Kuan-Hao and Chang, Kai-Wei and Peng, Nanyun},
     booktitle = {Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
     year = {2024}
   }
   

   Details

5. Event Detection from Social Media for Epidemic Prediction

   Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric J. Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, and Kai-Wei Chang, in Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024.
   Full Text Code BibTeX Details

   @inproceedings{parekh2024pipp,
     title = {Event Detection from Social Media for Epidemic Prediction},
     author = {Parekh, Tanmay and Mac, Anh and Yu, Jiarui and Dong, Yuxuan and Shahriar, Syed and Liu, Bonnie and Yang, Eric J and Huang, Kuan-Hao and Wang, Wei and Peng, Nanyun and Chang, Kai-Wei},
     booktitle = {Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
     year = {2024}
   }
   

   Details

6. Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies

   Kung-Hsiang Huang and Nanyun Peng, in The 3rd Workshop on Narrative Understanding (NAACL 2021), 2021.
   Full Text Abstract BibTeX Details

   Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extraction usually confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger. In this paper, we propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction. Experimental results show that our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.

   @inproceedings{huang2021document,
     title = {Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies},
     author = {Huang, Kung-Hsiang and Peng, Nanyun},
     booktitle = {The 3rd Workshop on Narrative Understanding (NAACL 2021)},
     year = {2021}
   }
   

   Details

7. Biomedical Event Extraction with Hierarchical Knowledge Graphs

   Kung-Hsiang Huang, Mu Yang, and Nanyun Peng, in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings, short, 2020.
   Full Text Slides Code Abstract BibTeX Details

   Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via a hierarchical graph representation encoded by a proposed Graph Edgeconditioned Attention Networks (GEANet). To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and 3.19% F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.

   @inproceedings{huang2020event,
     title = {Biomedical Event Extraction with Hierarchical Knowledge Graphs},
     author = {Huang, Kung-Hsiang and Yang, Mu and Peng, Nanyun},
     booktitle = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings, short},
     slideslive_id = {38940169},
     year = {2020}
   }
   

   Details