Jiayu Lin
2023
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
Jiayu Lin
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Rong Ye
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Meng Han
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Qi Zhang
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Ruofei Lai
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Xinyu Zhang
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Zhao Cao
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Xuanjing Huang
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Zhongyu Wei
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
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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Co-authors
- Rong Ye 1
- Meng Han 1
- Qi Zhang 1
- Ruofei Lai 1
- Xinyu Zhang 1
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