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.

这篇论文提出了一个名为 Americano 的新型框架，利用代理互动将生成过程分解为基于论证理论的连续动作序列，首先按顺序执行动作来生成论证性话语组成部分，然后根据这些组成部分生成最终论证。为了进一步模仿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模式，引入了一个论证改进模块，根据接收到的反馈自动评估和改进论证稿。通过在 Reddit/CMV 数据集的子集上进行反驳生成任务的评估，结果表明我们的方法优于端到端和连续思考提示方法，能够生成内容丰富、条理清晰、有说服力的各种论证。