@inproceedings{gangi-reddy-etal-2022-zero,
title = "A Zero-Shot Claim Detection Framework Using Question Answering",
author = "Gangi Reddy, Revanth and
Chinthakindi, Sai Chetan and
Fung, Yi R. and
Small, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.603",
pages = "6927--6933",
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).",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gangi-reddy-etal-2022-zero">
<titleInfo>
<title>A Zero-Shot Claim Detection Framework Using Question Answering</title>
</titleInfo>
<name type="personal">
<namePart type="given">Revanth</namePart>
<namePart type="family">Gangi Reddy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sai</namePart>
<namePart type="given">Chetan</namePart>
<namePart type="family">Chinthakindi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yi</namePart>
<namePart type="given">R</namePart>
<namePart type="family">Fung</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kevin</namePart>
<namePart type="family">Small</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Heng</namePart>
<namePart type="family">Ji</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 29th International Conference on Computational Linguistics</title>
</titleInfo>
<originInfo>
<publisher>International Committee on Computational Linguistics</publisher>
<place>
<placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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).</abstract>
<identifier type="citekey">gangi-reddy-etal-2022-zero</identifier>
<location>
<url>https://aclanthology.org/2022.coling-1.603</url>
</location>
<part>
<date>2022-10</date>
<extent unit="page">
<start>6927</start>
<end>6933</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A Zero-Shot Claim Detection Framework Using Question Answering
%A Gangi Reddy, Revanth
%A Chinthakindi, Sai Chetan
%A Fung, Yi R.
%A Small, Kevin
%A Ji, Heng
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F gangi-reddy-etal-2022-zero
%X 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).
%U https://aclanthology.org/2022.coling-1.603
%P 6927-6933
Markdown (Informal)
[A Zero-Shot Claim Detection Framework Using Question Answering](https://aclanthology.org/2022.coling-1.603) (Gangi Reddy et al., COLING 2022)
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.