QA-for-Event-Extraction

Model description

This is a QA model as part of the event extraction system in the ACL2021 paper: Zero-shot Event Extraction via Transfer Learning: Challenges and Insights. The pretrained architecture is roberta-large and the fine-tuning data is QAMR.

Demo

To see how the model works, type a question and a context separated in the right-hand-side textboxs under "Hosted inference API".

Example:

Question: Who was killed?
Context: A car bomb exploded Thursday in a crowded outdoor market in the heart of Jerusalem, killing at least two people, police said.
Answer: people

Usage

To use the QA model independently, follow the huggingface documentation on AutoModelForQuestionAnswering.
To use it as part of the event extraction system, please check out our Github repo.

BibTeX entry and citation info

@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.",
}