veronica320 commited on
Commit
9b5e9a6
1 Parent(s): 579279e

Create README.md

Browse files
Files changed (1) hide show
  1. README.md +30 -0
README.md ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # QA-for-Event-Extraction
2
+
3
+ ## Model description
4
+
5
+ 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).
6
+
7
+ ## Usage
8
+ - To use the QA model independently, follow the [huggingface documentation on AutoModelForQuestionAnswering](https://huggingface.co/transformers/task_summary.html?highlight=automodelforquestionanswering#extractive-question-answering).
9
+ - To use it as part of the event extraction system, please check out [our Github repo](https://github.com/veronica320/Zeroshot-Event-Extraction).
10
+
11
+
12
+ ### BibTeX entry and citation info
13
+ ```
14
+ @inproceedings{lyu-etal-2021-zero,
15
+ title = "Zero-shot Event Extraction via Transfer Learning: {C}hallenges and Insights",
16
+ author = "Lyu, Qing and
17
+ Zhang, Hongming and
18
+ Sulem, Elior and
19
+ Roth, Dan",
20
+ 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)",
21
+ month = aug,
22
+ year = "2021",
23
+ address = "Online",
24
+ publisher = "Association for Computational Linguistics",
25
+ url = "https://aclanthology.org/2021.acl-short.42",
26
+ doi = "10.18653/v1/2021.acl-short.42",
27
+ pages = "322--332",
28
+ 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.",
29
+ }
30
+ ```