AMERICANO: Argument Generation with Discourse-driven Decomposition and Agent Interaction
This addresses the problem of generating rigorous and organized arguments for NLP applications, but it is incremental as it builds on existing chain-of-thought and agent-based approaches.
The paper tackles the challenge of argument generation in NLP by proposing AMERICANO, a framework that decomposes the process using argumentation theory and agent interaction, resulting in outperforming end-to-end and chain-of-thought methods on a Reddit/CMV dataset for generating more coherent and persuasive arguments.
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.