🚀 事件抽取问答模型

本项目是一个问答模型，作为事件抽取系统的一部分，相关研究发表于ACL2021会议的论文：基于迁移学习的零样本事件抽取：挑战与见解。该模型采用预训练架构 roberta-large，并使用 QAMR 数据集进行微调。

🚀 快速开始

模型演示

若想查看模型的运行效果，可在“托管推理API”右侧的文本框中分别输入问题和上下文。

示例：

• 问题：谁被杀了？
• 上下文：警方称，周四，耶路撒冷市中心一个拥挤的露天市场发生汽车炸弹爆炸，造成至少两人死亡。
• 答案：人

模型使用

• 独立使用问答模型：请参考 huggingface关于AutoModelForQuestionAnswering的文档。
• 作为事件抽取系统的一部分使用：请查看 我们的Github仓库。

📚 详细文档

BibTeX引用和引用信息

@inproceedings{lyu-etal-2021-zero,
    title = "Zero-shot Event Extraction via Transfer Learning: {C}hallenges and Insights",
    author = "Lyu, Qing  and
      Zhang, Hongming  and
      Sulem, Elior  and
      Roth, Dan",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-short.42",
    doi = "10.18653/v1/2021.acl-short.42",
    pages = "322--332",
    abstract = "Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies. In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. {``}A city was attacked{''} entails {``}There is an attack{''}), exploiting pretrained TE/QA models for direct transfer. On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain. To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions.",
}