Argue with Me Tersely: Towards Sentence-Level Counter-Argument
  Generation
Jiayu Lin Rong Ye Meng Han Qi Zhang Ruofei Lai Xinyu Zhang Zhao Cao Xuanjing Huang Zhongyu Wei
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.