Argument generation is a challenging task in natural language processing, which requires rigorous reasoning and proper content organization. Inspired by recent chain-of-thought prompting that breaks down a complex task into intermediate steps, we propose Americano, a novel framework with agent interaction for argument generation. Our approach decomposes the generation process into sequential actions grounded on argumentation theory, which first executes actions sequentially to generate argumentative discourse components, and then produces a final argument conditioned on the components. To further mimic the human writing process and improve the left-to-right generation paradigm of current autoregressive language models, we introduce an argument refinement module which automatically evaluates and refines argument drafts based on feedback received. We evaluate our framework on the task of counterargument generation using a subset of Reddit/CMV dataset. The results show that our method outperforms both end-to-end and chain-of-thought prompting methods and can generate more coherent and persuasive arguments with diverse and rich contents.

翻译：论点生成是自然语言处理中的一项具有挑战性的任务，需要严谨的推理和恰当的内容组织。受近期将复杂任务分解为中间步骤的链式思维提示方法的启发，我们提出了Americano——一种基于智能体交互的论点生成新框架。我们的方法将生成过程分解为基于论证理论的序列化动作，首先按序执行动作以生成论证性话语组件，然后基于这些组件生成最终论点。为进一步模拟人类写作过程并改进当前自回归语言模型的从左到右生成范式，我们引入了一个论点精炼模块，该模块可根据收到的反馈自动评估并精炼论点草稿。我们利用Reddit/CMV数据集的一个子集，在反论点生成任务上评估了该框架。结果表明，我们的方法优于端到端方法和链式思维提示方法，能够生成更连贯、更有说服力且内容多样丰富的论点。