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        <title>Reinforced Sequence Training based Subjective Bias Correction</title>
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    <abstract>Subjective bias is ubiquitous on news sites, social media, and knowledge resources like Wikipedia. Many existing methods for subjective bias correction have typically focused on making one-word edits and have been trained over a single (often, noisy) domain. In contrast, we propose a novel reinforced sequence training approach for robust subjective bias correction. Three of the unique characteristics of the approach are: (i) it balances bias neutralization with fluency and semantics preservation through reinforcement learning, to broaden the scope to bias beyond a single word; (ii) it is cross-trained over multiple sources of bias to be more robust to new styles of biased writing that are not seen in the training data for a single domain; and (iii) it is used to fine-tune a large pre-trained transformer model to yield state-of-the-art performance in bias text correction task. Extensive experiments show that the proposed approach results in significant improvements in subjective bias correction versus alternatives.</abstract>
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    <identifier type="doi">10.18653/v1/2023.eacl-main.189</identifier>
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        <date>2023-05</date>
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%0 Conference Proceedings
%T Reinforced Sequence Training based Subjective Bias Correction
%A Madanagopal, Karthic
%A Caverlee, James
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F madanagopal-caverlee-2023-reinforced
%X Subjective bias is ubiquitous on news sites, social media, and knowledge resources like Wikipedia. Many existing methods for subjective bias correction have typically focused on making one-word edits and have been trained over a single (often, noisy) domain. In contrast, we propose a novel reinforced sequence training approach for robust subjective bias correction. Three of the unique characteristics of the approach are: (i) it balances bias neutralization with fluency and semantics preservation through reinforcement learning, to broaden the scope to bias beyond a single word; (ii) it is cross-trained over multiple sources of bias to be more robust to new styles of biased writing that are not seen in the training data for a single domain; and (iii) it is used to fine-tune a large pre-trained transformer model to yield state-of-the-art performance in bias text correction task. Extensive experiments show that the proposed approach results in significant improvements in subjective bias correction versus alternatives.
%R 10.18653/v1/2023.eacl-main.189
%U https://aclanthology.org/2023.eacl-main.189
%U https://doi.org/10.18653/v1/2023.eacl-main.189
%P 2585-2598

Markdown (Informal)

[Reinforced Sequence Training based Subjective Bias Correction](https://aclanthology.org/2023.eacl-main.189) (Madanagopal & Caverlee, EACL 2023)

• Reinforced Sequence Training based Subjective Bias Correction (Madanagopal & Caverlee, EACL 2023)

ACL

• Karthic Madanagopal and James Caverlee. 2023. Reinforced Sequence Training based Subjective Bias Correction. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2585–2598, Dubrovnik, Croatia. Association for Computational Linguistics.