A Zero-Shot Claim Detection Framework Using Question Answering

Revanth Gangi Reddy, Sai Chetan Chinthakindi, Yi R. Fung, Kevin Small, Heng Ji


Abstract
In recent years, there has been an increasing interest in claim detection as an important building block for misinformation detection. This involves detecting more fine-grained attributes relating to the claim, such as the claimer, claim topic, claim object pertaining to the topic, etc. Yet, a notable bottleneck of existing claim detection approaches is their portability to emerging events and low-resource training data settings. In this regard, we propose a fine-grained claim detection framework that leverages zero-shot Question Answering (QA) using directed questions to solve a diverse set of sub-tasks such as topic filtering, claim object detection, and claimer detection. We show that our approach significantly outperforms various zero-shot, few-shot and task-specific baselines on the NewsClaims benchmark (Reddy et al., 2021).
Anthology ID:
2022.coling-1.603
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6927–6933
Language:
URL:
https://aclanthology.org/2022.coling-1.603
DOI:
Bibkey:
Cite (ACL):
Revanth Gangi Reddy, Sai Chetan Chinthakindi, Yi R. Fung, Kevin Small, and Heng Ji. 2022. A Zero-Shot Claim Detection Framework Using Question Answering. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6927–6933, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Zero-Shot Claim Detection Framework Using Question Answering (Gangi Reddy et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.603.pdf
Data
Natural Questions