Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation

• URL: http://arxiv.org/abs/2312.13608v1
• Date: Thu, 21 Dec 2023 06:51:34 GMT
• Title: Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
• Authors: Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei
• Abstract summary: We present the ArgTersely benchmark for sentence-level counter-argument generation. We also propose Arg-LlaMA for generating high-quality counter-argument.
• Score: 62.069374456021016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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