Argue With Me Tersely: Towards Sentence-level Counter-argument Generation

Lin Jiayu, Ye Rong, Han Meng, Zhang Qi, Lai Ruofei, Zhang Xinyu, Cao Zhao, Huang Xuanjing, Wei Zhongyu. Arxiv 2023

[Paper] [Code]    
BERT GPT Has Code Model Architecture RAG Reinforcement Learning Tools

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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