Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. For example, persuading someone to reconsider smoking might involve empathetic, well founded arguments based on facts and expert opinions about its ill-effects and the consequences on one's family. However, the automatic generation of high-quality factual arguments can be challenging. Addressing existing controllability issues can make the recent advances in computational models for argument generation a potential solution. In this paper, we introduce ArgU: a neural argument generator capable of producing factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure using Walton's argument scheme-based control codes. Unfortunately, computational argument generation is a relatively new field and lacks datasets conducive to training. Hence, we have compiled and released an annotated corpora of 69,428 arguments spanning six topics and six argument schemes, making it the largest publicly available corpus for identifying argument schemes; the paper details our annotation and dataset creation framework. We further experiment with an argument generation strategy that establishes an inference strategy by generating an ``argument template'' before actual argument generation. Our results demonstrate that it is possible to automatically generate diverse arguments exhibiting different inference patterns for the same set of facts by using control codes based on argument schemes and stance.

翻译：有效的论证对于实现目标明确、结果理想的对话至关重要。例如，劝说某人重新考虑吸烟问题，可能需要基于事实和专家意见，围绕吸烟的危害及其对家庭的影响，提出富有同理心且论证充分的论点。然而，自动生成高质量的事实性论点颇具挑战。解决现有可控性问题，可使近期计算模型在论点生成领域的进展成为潜在解决方案。本文提出ArgU：一种基于输入事实与现实世界概念生成事实性论点的神经论点生成器，可通过基于沃尔顿论证方案的控制编码，对论点的立场和论证结构进行显式控制。遗憾的是，计算化论点生成作为新兴领域，缺乏适于训练的公开数据集。为此，我们整理并发布了涵盖六个主题和六种论证方案的69,428条论点标注语料库，使其成为目前规模最大的公开论证方案识别语料库；本文详细阐述了标注及数据集构建框架。我们进一步实验了一种论点生成策略：在实际生成论点前，先通过生成"论点模板"建立推理策略。结果表明，利用基于论证方案和立场的控制编码，能够自动为同一组事实生成呈现不同推理模式的多样化论点。