| # 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](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co/roberta-large) and the fine-tuning data is [QAMR](https://github.com/uwnlp/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](https://huggingface.co/transformers/task_summary.html?highlight=automodelforquestionanswering#extractive-question-answering). | |
| - To use it as part of the event extraction system, please check out [our Github repo](https://github.com/veronica320/Zeroshot-Event-Extraction). | |
| ### 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.", | |
| } | |
| ``` |