Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation (2024)

Abstract: Argumentative essay generation (AEG) aims to generate complete texts on specific controversial topics or debates. Although current AEG methods can generate individual opinions, they often overlook the high-level connections between these opinions. This often leads to the generated results being mired in logical confusion, unable to proof their own arguments effectively. The generated essay may present evidence that contradicts the claims or they may fail to assemble the claims into logical flow. In this paper, we present a unified two-stage framework: Proof-Enhancement and Self-Annotation (PESA) for AEG with a focus on logical enhancement. Specifically, we first construct pseudo-labels for logical information,claims and grounds, using a large language model. We then propose a tree planning approach that introduces proof principles and ensures logical consistency. Extensive experimental results show that, benefiting from proof principle guidance, PESA generates argumentative essays with better logical validity and persuasiveness than strong baseline models.

## introduction from decision-making to public discussions, argumentative texts serve to proclaim ideas or defend a point of view in a wide range of scenarios @xcite . argumentative essay generation (aeg) is a task designed to generate a persuasive argumentative text containing several arguments on a given controversial debate topic (e.g., whether euthanasia should be legalized). @xcite introduced a keywords text-planning method to the aeg task. the plan-and-write paradigm used in this method enriches the length and content of argumentative text, yielding impressive results. however, due to the lack of logical and proof guidance in planning, current aeg methods tend to overlook the highlevel connections between individual opinions. ## conclusion in this paper, we present a unified two-stage framework (pesa) for aeg with a focus on proof and logical correctness. the framework is composed of two components: the self-annotation method leverages a large language model to progressively extract logical information from aeg data, obtaining high-quality training data; the proof-enhancement method enhances the persuasive power of generative results by explicitly incorporating proof principles into the planning process, enabling the language model to produce argumentative texts in line with the toulmin argumentation model. automatic and manual evaluation metrics show that our method could produce fluent, coherent, and persuasive argumentative text with a significant improvement in the logical structure.