@inproceedings{furman-etal-2023-high,
title = "High-quality argumentative information in low resources approaches improve counter-narrative generation",
author = "Furman, Dami{\'a}n and
Torres, Pablo and
Rodr{\'\i}guez, Jos{\'e} and
Letzen, Diego and
Martinez, Maria and
Alemany, Laura",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.194",
doi = "10.18653/v1/2023.findings-emnlp.194",
pages = "2942--2956",
abstract = "It has been shown that high quality fine-tuning boosts the performance of language models, even if the size of the fine-tuning is small. In this work we show how highly targeted fine-tuning improves the task of hate speech counter-narrative generation in user-generated text, even for very small sizes of training (1722 counter-narratives for English and 355 for Spanish). Providing a small subset of examples focusing on single argumentative strategies, together with the argumentative analysis relevant to that strategy, yields counter-narratives that are as satisfactory as providing the whole set of counter-narratives. We also show that a good base model is required for the fine-tuning to have a positive impact. Indeed, for Spanish, the counter-narratives obtained without fine-tuning are mostly unacceptable, and, while fine-tuning improves their overall quality, the performance still remains quite unsatisfactory.",
}
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<abstract>It has been shown that high quality fine-tuning boosts the performance of language models, even if the size of the fine-tuning is small. In this work we show how highly targeted fine-tuning improves the task of hate speech counter-narrative generation in user-generated text, even for very small sizes of training (1722 counter-narratives for English and 355 for Spanish). Providing a small subset of examples focusing on single argumentative strategies, together with the argumentative analysis relevant to that strategy, yields counter-narratives that are as satisfactory as providing the whole set of counter-narratives. We also show that a good base model is required for the fine-tuning to have a positive impact. Indeed, for Spanish, the counter-narratives obtained without fine-tuning are mostly unacceptable, and, while fine-tuning improves their overall quality, the performance still remains quite unsatisfactory.</abstract>
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%0 Conference Proceedings
%T High-quality argumentative information in low resources approaches improve counter-narrative generation
%A Furman, Damián
%A Torres, Pablo
%A Rodríguez, José
%A Letzen, Diego
%A Martinez, Maria
%A Alemany, Laura
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F furman-etal-2023-high
%X It has been shown that high quality fine-tuning boosts the performance of language models, even if the size of the fine-tuning is small. In this work we show how highly targeted fine-tuning improves the task of hate speech counter-narrative generation in user-generated text, even for very small sizes of training (1722 counter-narratives for English and 355 for Spanish). Providing a small subset of examples focusing on single argumentative strategies, together with the argumentative analysis relevant to that strategy, yields counter-narratives that are as satisfactory as providing the whole set of counter-narratives. We also show that a good base model is required for the fine-tuning to have a positive impact. Indeed, for Spanish, the counter-narratives obtained without fine-tuning are mostly unacceptable, and, while fine-tuning improves their overall quality, the performance still remains quite unsatisfactory.
%R 10.18653/v1/2023.findings-emnlp.194
%U https://aclanthology.org/2023.findings-emnlp.194
%U https://doi.org/10.18653/v1/2023.findings-emnlp.194
%P 2942-2956
Markdown (Informal)
[High-quality argumentative information in low resources approaches improve counter-narrative generation](https://aclanthology.org/2023.findings-emnlp.194) (Furman et al., Findings 2023)
ACL