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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.clib-1.13.pdf