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.

本文介绍了 ArgU：一种神经论证生成器，可从输入事实和世界真实概念中生成事实论据，并使用 Walton 的基于辩证方案的控制代码明确控制其立场和论证结构。此外，我们还发布了一个包括 69,428 个论据的注释语料库，跨足了六个话题和六个论证方案，并进一步进行了实验，以构建了一种推理策略，通过生成 “论证模板” 在实际论证生成之前生成，以展示使用基于辩证方案和立场的控制代码可以自动生成不同推理模式的多样化论据。