Automatic argument generation is an appealing but challenging task. In this
paper, we study the specific problem of counter-argument generation, and
present a novel framework, CANDELA. It consists of a powerful retrieval system
and a novel two-step generation model, where a text planning decoder first
decides on the main talking points and a proper language style for each
sentence, then a content realization decoder reflects the decisions and
constructs an informative paragraph-level argument. Furthermore, our generation
model is empowered by a retrieval system indexed with 12 million articles
collected from Wikipedia and popular English news media, which provides access
to high-quality content with diversity. Automatic evaluation on a large-scale
dataset collected from Reddit shows that our model yields significantly higher
BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial
comparisons. Human evaluation further indicates that our system arguments are
more appropriate for refutation and richer in content.

本文介绍了一个新的框架 CANDELA，用来解决针对特定问题的对抗论证自动生成，它包含了一个强大的检索系统和一个新型的双步骤生成模型，通过索引 1200 万文章并使用自然语言处理，使得自动生成的对抗论证比以往更为适当和丰富。经过自动评估和人工评估，该模型均取得了相比现有技术的更高 BLEU、ROUGE 和 METEOR 语言分数。