Zero-shot Event Causality Identification with Question Answering

Daria Liakhovets, Sven Schlarb


Abstract
Extraction of event causality and especially implicit causality from text data is a challenging task. Causality is often treated as a specific relation type and can be considered as a part of relation extraction or relation classification task. Many causality identification-related tasks are designed to select the most plausible alternative of a set of possible causes and consider multiple-choice classification settings. Since there are powerful Question Answering (QA) systems pretrained on large text corpora, we investigated a zero-shot QA-based approach for event causality extraction using a Wikipedia-based dataset containing event descriptions (articles) and annotated causes. We aimed to evaluate to what extent reading comprehension ability of the QA-pipeline can be used for event-related causality extraction from plain text without any additional training. Some evaluation challenges and limitations of the data were discussed. We compared the performance of a two-step pipeline consisting of passage retrieval and extractive QA with QA-only pipeline on event-associated articles and mixed ones. Our systems achieved average cosine semantic similarity scores of 44 – 45% in different settings.
Anthology ID:
2022.clib-1.13
Volume:
Proceedings of the 5th International Conference on Computational Linguistics in Bulgaria (CLIB 2022)
Month:
September
Year:
2022
Address:
Sofia, Bulgaria
Venue:
CLIB
SIG:
Publisher:
Department of Computational Linguistics, IBL -- BAS
Note:
Pages:
113–119
Language:
URL:
https://aclanthology.org/2022.clib-1.13
DOI:
Bibkey:
Cite (ACL):
Daria Liakhovets and Sven Schlarb. 2022. Zero-shot Event Causality Identification with Question Answering. In Proceedings of the 5th International Conference on Computational Linguistics in Bulgaria (CLIB 2022), pages 113–119, Sofia, Bulgaria. Department of Computational Linguistics, IBL -- BAS.
Cite (Informal):
Zero-shot Event Causality Identification with Question Answering (Liakhovets & Schlarb, CLIB 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.clib-1.13.pdf