QA For Event Extraction

🚀 事件抽取問答模型

本項目是一個問答模型，作為事件抽取系統的一部分，相關研究發表於ACL2021會議的論文：基於遷移學習的零樣本事件抽取：挑戰與見解。該模型採用預訓練架構 roberta-large，並使用 QAMR 數據集進行微調。

🚀 快速開始

模型演示

若想查看模型的運行效果，可在“託管推理API”右側的文本框中分別輸入問題和上下文。

示例：

• 問題：誰被殺了？
• 上下文：警方稱，週四，耶路撒冷市中心一個擁擠的露天市場發生汽車炸彈爆炸，造成至少兩人死亡。
• 答案：人

模型使用

• 獨立使用問答模型：請參考 huggingface關於AutoModelForQuestionAnswering的文檔。
• 作為事件抽取系統的一部分使用：請查看 我們的Github倉庫。

📚 詳細文檔

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.",
}