@inproceedings{lin-etal-2023-argue,
title = "Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation",
author = "Lin, Jiayu and
Ye, Rong and
Han, Meng and
Zhang, Qi and
Lai, Ruofei and
Zhang, Xinyu and
Cao, Zhao and
Huang, Xuanjing and
Wei, Zhongyu",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.1039",
doi = "10.18653/v1/2023.emnlp-main.1039",
pages = "16705--16720",
abstract = "Counter-argument generation{---}a captivating area in computational linguistics{---}seeks to craft statements that offer opposing views. While most research has ventured into paragraph-level generation, sentence-level counter-argument generation beckons with its unique constraints and brevity-focused challenges. Furthermore, the diverse nature of counter-arguments poses challenges for evaluating model performance solely based on n-gram-based metrics. In this paper, we present the ArgTersely benchmark for sentence-level counter-argument generation, drawing from a manually annotated dataset from the ChangeMyView debate forum. We also propose Arg-LlaMA for generating high-quality counter-argument. For better evaluation, we trained a BERT-based evaluator Arg-Judge with human preference data. We conducted comparative experiments involving various baselines such as LlaMA, Alpaca, GPT-3, and others. The results show the competitiveness of our proposed framework and evaluator in counter-argument generation tasks. Code and data are available at https://github.com/amazingljy1206/ArgTersely.",
}
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<abstract>Counter-argument generation—a captivating area in computational linguistics—seeks to craft statements that offer opposing views. While most research has ventured into paragraph-level generation, sentence-level counter-argument generation beckons with its unique constraints and brevity-focused challenges. Furthermore, the diverse nature of counter-arguments poses challenges for evaluating model performance solely based on n-gram-based metrics. In this paper, we present the ArgTersely benchmark for sentence-level counter-argument generation, drawing from a manually annotated dataset from the ChangeMyView debate forum. We also propose Arg-LlaMA for generating high-quality counter-argument. For better evaluation, we trained a BERT-based evaluator Arg-Judge with human preference data. We conducted comparative experiments involving various baselines such as LlaMA, Alpaca, GPT-3, and others. The results show the competitiveness of our proposed framework and evaluator in counter-argument generation tasks. Code and data are available at https://github.com/amazingljy1206/ArgTersely.</abstract>
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%0 Conference Proceedings
%T Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
%A Lin, Jiayu
%A Ye, Rong
%A Han, Meng
%A Zhang, Qi
%A Lai, Ruofei
%A Zhang, Xinyu
%A Cao, Zhao
%A Huang, Xuanjing
%A Wei, Zhongyu
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lin-etal-2023-argue
%X Counter-argument generation—a captivating area in computational linguistics—seeks to craft statements that offer opposing views. While most research has ventured into paragraph-level generation, sentence-level counter-argument generation beckons with its unique constraints and brevity-focused challenges. Furthermore, the diverse nature of counter-arguments poses challenges for evaluating model performance solely based on n-gram-based metrics. In this paper, we present the ArgTersely benchmark for sentence-level counter-argument generation, drawing from a manually annotated dataset from the ChangeMyView debate forum. We also propose Arg-LlaMA for generating high-quality counter-argument. For better evaluation, we trained a BERT-based evaluator Arg-Judge with human preference data. We conducted comparative experiments involving various baselines such as LlaMA, Alpaca, GPT-3, and others. The results show the competitiveness of our proposed framework and evaluator in counter-argument generation tasks. Code and data are available at https://github.com/amazingljy1206/ArgTersely.
%R 10.18653/v1/2023.emnlp-main.1039
%U https://aclanthology.org/2023.emnlp-main.1039
%U https://doi.org/10.18653/v1/2023.emnlp-main.1039
%P 16705-16720
Markdown (Informal)
[Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation](https://aclanthology.org/2023.emnlp-main.1039) (Lin et al., EMNLP 2023)
ACL
- Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, and Zhongyu Wei. 2023. Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16705–16720, Singapore. Association for Computational Linguistics.