@inproceedings{garcia-gilabert-etal-2024-resetox,
title = "{R}e{S}e{TOX}: Re-learning attention weights for toxicity mitigation in machine translation",
author = "Garc{\'\i}a Gilabert, Javier and
Escolano, Carlos and
Costa-juss{\`a}, Marta",
editor = "Scarton, Carolina and
Prescott, Charlotte and
Bayliss, Chris and
Oakley, Chris and
Wright, Joanna and
Wrigley, Stuart and
Song, Xingyi and
Gow-Smith, Edward and
Bawden, Rachel and
S{\'a}nchez-Cartagena, V{\'\i}ctor M and
Cadwell, Patrick and
Lapshinova-Koltunski, Ekaterina and
Cabarr{\~a}o, Vera and
Chatzitheodorou, Konstantinos and
Nurminen, Mary and
Kanojia, Diptesh and
Moniz, Helena",
booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)",
month = jun,
year = "2024",
address = "Sheffield, UK",
publisher = "European Association for Machine Translation (EAMT)",
url = "https://aclanthology.org/2024.eamt-1.8",
pages = "37--58",
abstract = "Our proposed method, RESETOX (REdoSEarch if TOXic), addresses the issue ofNeural Machine Translation (NMT) gener-ating translation outputs that contain toxicwords not present in the input. The ob-jective is to mitigate the introduction oftoxic language without the need for re-training. In the case of identified addedtoxicity during the inference process, RE-SETOX dynamically adjusts the key-valueself-attention weights and re-evaluates thebeam search hypotheses. Experimental re-sults demonstrate that RESETOX achievesa remarkable 57{\%} reduction in added tox-icity while maintaining an average trans-lation quality of 99.5{\%} across 164 lan-guages. Our code is available at: https://github.com",
}
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<abstract>Our proposed method, RESETOX (REdoSEarch if TOXic), addresses the issue ofNeural Machine Translation (NMT) gener-ating translation outputs that contain toxicwords not present in the input. The ob-jective is to mitigate the introduction oftoxic language without the need for re-training. In the case of identified addedtoxicity during the inference process, RE-SETOX dynamically adjusts the key-valueself-attention weights and re-evaluates thebeam search hypotheses. Experimental re-sults demonstrate that RESETOX achievesa remarkable 57% reduction in added tox-icity while maintaining an average trans-lation quality of 99.5% across 164 lan-guages. Our code is available at: https://github.com</abstract>
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%0 Conference Proceedings
%T ReSeTOX: Re-learning attention weights for toxicity mitigation in machine translation
%A García Gilabert, Javier
%A Escolano, Carlos
%A Costa-jussà, Marta
%Y Scarton, Carolina
%Y Prescott, Charlotte
%Y Bayliss, Chris
%Y Oakley, Chris
%Y Wright, Joanna
%Y Wrigley, Stuart
%Y Song, Xingyi
%Y Gow-Smith, Edward
%Y Bawden, Rachel
%Y Sánchez-Cartagena, Víctor M.
%Y Cadwell, Patrick
%Y Lapshinova-Koltunski, Ekaterina
%Y Cabarrão, Vera
%Y Chatzitheodorou, Konstantinos
%Y Nurminen, Mary
%Y Kanojia, Diptesh
%Y Moniz, Helena
%S Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, UK
%F garcia-gilabert-etal-2024-resetox
%X Our proposed method, RESETOX (REdoSEarch if TOXic), addresses the issue ofNeural Machine Translation (NMT) gener-ating translation outputs that contain toxicwords not present in the input. The ob-jective is to mitigate the introduction oftoxic language without the need for re-training. In the case of identified addedtoxicity during the inference process, RE-SETOX dynamically adjusts the key-valueself-attention weights and re-evaluates thebeam search hypotheses. Experimental re-sults demonstrate that RESETOX achievesa remarkable 57% reduction in added tox-icity while maintaining an average trans-lation quality of 99.5% across 164 lan-guages. Our code is available at: https://github.com
%U https://aclanthology.org/2024.eamt-1.8
%P 37-58
Markdown (Informal)
[ReSeTOX: Re-learning attention weights for toxicity mitigation in machine translation](https://aclanthology.org/2024.eamt-1.8) (García Gilabert et al., EAMT 2024)
ACL