Fine-grained Event Classification in News-like Text Snippets - Shared Task 2, CASE 2021

Jacek Haneczok, Guillaume Jacquet, Jakub Piskorski, Nicolas Stefanovitch


Abstract
This paper describes the Shared Task on Fine-grained Event Classification in News-like Text Snippets. The Shared Task is divided into three sub-tasks: (a) classification of text snippets reporting socio-political events (25 classes) for which vast amount of training data exists, although exhibiting different structure and style vis-a-vis test data, (b) enhancement to a generalized zero-shot learning problem, where 3 additional event types were introduced in advance, but without any training data (‘unseen’ classes), and (c) further extension, which introduced 2 additional event types, announced shortly prior to the evaluation phase. The reported Shared Task focuses on classification of events in English texts and is organized as part of the Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), co-located with the ACL-IJCNLP 2021 Conference. Four teams participated in the task. Best performing systems for the three aforementioned sub-tasks achieved 83.9%, 79.7% and 77.1% weighted F1 scores respectively.
Anthology ID:
2021.case-1.23
Volume:
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | CASE | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
179–192
Language:
URL:
https://aclanthology.org/2021.case-1.23
DOI:
10.18653/v1/2021.case-1.23
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.case-1.23.pdf