@inproceedings{hatua-etal-2021-claim,
title = "Claim Verification Using a Multi-{GAN} Based Model",
author = "Hatua, Amartya and
Mukherjee, Arjun and
Verma, Rakesh",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.56",
pages = "494--503",
abstract = "This article describes research on claim verification carried out using a multiple GAN-based model. The proposed model consists of three pairs of generators and discriminators. The generator and discriminator pairs are responsible for generating synthetic data for supported and refuted claims and claim labels. A theoretical discussion about the proposed model is provided to validate the equilibrium state of the model. The proposed model is applied to the FEVER dataset, and a pre-trained language model is used for the input text data. The synthetically generated data helps to gain information that improves classification performance over state of the art baselines. The respective F1 scores after applying the proposed method on FEVER 1.0 and FEVER 2.0 datasets are 0.65+-0.018 and 0.65+-0.051.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hatua-etal-2021-claim">
<titleInfo>
<title>Claim Verification Using a Multi-GAN Based Model</title>
</titleInfo>
<name type="personal">
<namePart type="given">Amartya</namePart>
<namePart type="family">Hatua</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Arjun</namePart>
<namePart type="family">Mukherjee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rakesh</namePart>
<namePart type="family">Verma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ruslan</namePart>
<namePart type="family">Mitkov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Galia</namePart>
<namePart type="family">Angelova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>INCOMA Ltd.</publisher>
<place>
<placeTerm type="text">Held Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This article describes research on claim verification carried out using a multiple GAN-based model. The proposed model consists of three pairs of generators and discriminators. The generator and discriminator pairs are responsible for generating synthetic data for supported and refuted claims and claim labels. A theoretical discussion about the proposed model is provided to validate the equilibrium state of the model. The proposed model is applied to the FEVER dataset, and a pre-trained language model is used for the input text data. The synthetically generated data helps to gain information that improves classification performance over state of the art baselines. The respective F1 scores after applying the proposed method on FEVER 1.0 and FEVER 2.0 datasets are 0.65+-0.018 and 0.65+-0.051.</abstract>
<identifier type="citekey">hatua-etal-2021-claim</identifier>
<location>
<url>https://aclanthology.org/2021.ranlp-1.56</url>
</location>
<part>
<date>2021-09</date>
<extent unit="page">
<start>494</start>
<end>503</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Claim Verification Using a Multi-GAN Based Model
%A Hatua, Amartya
%A Mukherjee, Arjun
%A Verma, Rakesh
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F hatua-etal-2021-claim
%X This article describes research on claim verification carried out using a multiple GAN-based model. The proposed model consists of three pairs of generators and discriminators. The generator and discriminator pairs are responsible for generating synthetic data for supported and refuted claims and claim labels. A theoretical discussion about the proposed model is provided to validate the equilibrium state of the model. The proposed model is applied to the FEVER dataset, and a pre-trained language model is used for the input text data. The synthetically generated data helps to gain information that improves classification performance over state of the art baselines. The respective F1 scores after applying the proposed method on FEVER 1.0 and FEVER 2.0 datasets are 0.65+-0.018 and 0.65+-0.051.
%U https://aclanthology.org/2021.ranlp-1.56
%P 494-503
Markdown (Informal)
[Claim Verification Using a Multi-GAN Based Model](https://aclanthology.org/2021.ranlp-1.56) (Hatua et al., RANLP 2021)
ACL
- Amartya Hatua, Arjun Mukherjee, and Rakesh Verma. 2021. Claim Verification Using a Multi-GAN Based Model. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 494–503, Held Online. INCOMA Ltd..