@inproceedings{zhao-etal-2024-zerostance,
title = "{Z}ero{S}tance: Leveraging {C}hat{GPT} for Open-Domain Stance Detection via Dataset Generation",
author = "Zhao, Chenye and
Li, Yingjie and
Caragea, Cornelia and
Zhang, Yue",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.794",
doi = "10.18653/v1/2024.findings-acl.794",
pages = "13390--13405",
abstract = "Zero-shot stance detection that aims to detect the stance (typically against, favor, or neutral) towards unseen targets has attracted considerable attention. However, most previous studies only focus on targets from a single or limited text domains (e.g., financial domain), and thus zero-shot models cannot generalize well to unseen targets of diverse domains (e.g., political domain). In this paper, we consider a more realistic task, i.e., open-domain stance detection, which aims at training a model that is able to generalize well to unseen targets across multiple domains of interest. Particularly, we propose a novel dataset generation method ZeroStance, which leverages ChatGPT to construct a synthetic open-domain dataset CHATStance that covers a wide range of domains. We then train an open-domain model on our synthetic dataset after proper data filtering. Extensive results indicate that our model, when trained on this synthetic dataset, shows superior generalization to unseen targets of diverse domains over baselines on most benchmarks. Our method requires only a task description in the form of a prompt and is much more cost-effective and data-efficient than previous methods. We will release our code and data to facilitate future research.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhao-etal-2024-zerostance">
<titleInfo>
<title>ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chenye</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yingjie</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Cornelia</namePart>
<namePart type="family">Caragea</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yue</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2024</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lun-Wei</namePart>
<namePart type="family">Ku</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andre</namePart>
<namePart type="family">Martins</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vivek</namePart>
<namePart type="family">Srikumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Bangkok, Thailand</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Zero-shot stance detection that aims to detect the stance (typically against, favor, or neutral) towards unseen targets has attracted considerable attention. However, most previous studies only focus on targets from a single or limited text domains (e.g., financial domain), and thus zero-shot models cannot generalize well to unseen targets of diverse domains (e.g., political domain). In this paper, we consider a more realistic task, i.e., open-domain stance detection, which aims at training a model that is able to generalize well to unseen targets across multiple domains of interest. Particularly, we propose a novel dataset generation method ZeroStance, which leverages ChatGPT to construct a synthetic open-domain dataset CHATStance that covers a wide range of domains. We then train an open-domain model on our synthetic dataset after proper data filtering. Extensive results indicate that our model, when trained on this synthetic dataset, shows superior generalization to unseen targets of diverse domains over baselines on most benchmarks. Our method requires only a task description in the form of a prompt and is much more cost-effective and data-efficient than previous methods. We will release our code and data to facilitate future research.</abstract>
<identifier type="citekey">zhao-etal-2024-zerostance</identifier>
<identifier type="doi">10.18653/v1/2024.findings-acl.794</identifier>
<location>
<url>https://aclanthology.org/2024.findings-acl.794</url>
</location>
<part>
<date>2024-08</date>
<extent unit="page">
<start>13390</start>
<end>13405</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation
%A Zhao, Chenye
%A Li, Yingjie
%A Caragea, Cornelia
%A Zhang, Yue
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhao-etal-2024-zerostance
%X Zero-shot stance detection that aims to detect the stance (typically against, favor, or neutral) towards unseen targets has attracted considerable attention. However, most previous studies only focus on targets from a single or limited text domains (e.g., financial domain), and thus zero-shot models cannot generalize well to unseen targets of diverse domains (e.g., political domain). In this paper, we consider a more realistic task, i.e., open-domain stance detection, which aims at training a model that is able to generalize well to unseen targets across multiple domains of interest. Particularly, we propose a novel dataset generation method ZeroStance, which leverages ChatGPT to construct a synthetic open-domain dataset CHATStance that covers a wide range of domains. We then train an open-domain model on our synthetic dataset after proper data filtering. Extensive results indicate that our model, when trained on this synthetic dataset, shows superior generalization to unseen targets of diverse domains over baselines on most benchmarks. Our method requires only a task description in the form of a prompt and is much more cost-effective and data-efficient than previous methods. We will release our code and data to facilitate future research.
%R 10.18653/v1/2024.findings-acl.794
%U https://aclanthology.org/2024.findings-acl.794
%U https://doi.org/10.18653/v1/2024.findings-acl.794
%P 13390-13405
Markdown (Informal)
[ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation](https://aclanthology.org/2024.findings-acl.794) (Zhao et al., Findings 2024)
ACL