SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models

Anik Saha, Alex Gittens, Jian Ni, Oktie Hassanzadeh, Bulent Yener, Kavitha Srinivas


Abstract
Understanding causal relationship is an importance part of natural language processing. We address the causal information extraction problem with different neural models built on top of pre-trained transformer-based language models for identifying Cause, Effect and Signal spans, from news data sets. We use the Causal News Corpus subtask 2 training data set to train span-based and sequence tagging models. Our span-based model based on pre-trained BERT base weights achieves an F1 score of 47.48 on the test set with an accuracy score of 36.87 and obtained 3rd place in the Causal News Corpus 2022 shared task.
Anthology ID:
2022.case-1.18
Volume:
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Ali Hürriyetoğlu, Hristo Tanev, Vanni Zavarella, Erdem Yörük
Venue:
CASE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
133–137
Language:
URL:
https://aclanthology.org/2022.case-1.18
DOI:
10.18653/v1/2022.case-1.18
Bibkey:
Cite (ACL):
Anik Saha, Alex Gittens, Jian Ni, Oktie Hassanzadeh, Bulent Yener, and Kavitha Srinivas. 2022. SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models. In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pages 133–137, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models (Saha et al., CASE 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.case-1.18.pdf
Video:
 https://aclanthology.org/2022.case-1.18.mp4