AMERICANO: Argument Generation with Discourse-driven Decomposition and
  Agent Interaction
Zhe Hu Hou Pong Chan Yu Yin
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.