TY - JOUR
T1 - A Zero-Shot Claim Detection Framework using Question Answering
AU - Reddy, Revanth Gangi
AU - Chetan, Sai
AU - Fung, Yi R.
AU - Small, Kevin
AU - Ji, Heng
N1 - This research is based upon work supported by U.S. DARPA AIDA Program No. FA8750-18-2-0014. FA8750-19-2-1004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
PY - 2022
Y1 - 2022
N2 - 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 approach1 significantly outperforms various zero-shot, few-shot and task-specific baselines on the NEWSCLAIMS benchmark (Reddy et al., 2021).
AB - 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 approach1 significantly outperforms various zero-shot, few-shot and task-specific baselines on the NEWSCLAIMS benchmark (Reddy et al., 2021).
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M3 - Conference article
AN - SCOPUS:85152266152
SN - 2951-2093
VL - 29
SP - 6927
EP - 6933
JO - Proceedings - International Conference on Computational Linguistics, COLING
JF - Proceedings - International Conference on Computational Linguistics, COLING
IS - 1
T2 - 29th International Conference on Computational Linguistics, COLING 2022
Y2 - 12 October 2022 through 17 October 2022
ER -