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 · 2023
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 ngram-based metrics.In this paper, we present the ArgTersely benchmark for sentence-level counter-argument generation, drawing from a manually annotated dataset from the Change-MyView debate forum 1 .We also propose Arg-LlaMA for generating high-quality counterargument.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.