Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
This addresses the problem of generating concise opposing views in debates for computational linguistics, though it is incremental as it builds on existing paragraph-level work.
The paper tackles sentence-level counter-argument generation by introducing the ArgTersely benchmark from annotated debate data and proposing Arg-LlaMA for generation and Arg-Judge for evaluation, showing competitive results against baselines like GPT-3.
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.