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.",
}