An optimal delivery of arguments is key to persuasion in any debate, both for humans and for AI systems. This requires the use of clear and fluent claims relevant to the given debate. Prior work has studied the automatic assessment of argument quality extensively. Yet, no approach actually improves the quality so far. To fill this gap, this paper proposes the task of claim optimization: to rewrite argumentative claims in order to optimize their delivery. As multiple types of optimization are possible, we approach this task by first generating a diverse set of candidate claims using a large language model, such as BART, taking into account contextual information. Then, the best candidate is selected using various quality metrics. In automatic and human evaluation on an English-language corpus, our quality-based candidate selection outperforms several baselines, improving 60% of all claims (worsening 16% only). Follow-up analyses reveal that, beyond copy editing, our approach often specifies claims with details, whereas it adds less evidence than humans do. Moreover, its capabilities generalize well to other domains, such as instructional texts.

翻译：论点的最优表达在任何辩论中都是说服力的关键，无论是对人类还是AI系统而言。这要求使用与特定辩论相关的清晰流畅的论点。此前研究已广泛探讨了论证质量的自动评估，但尚未有方法实际提升其质量。为填补这一空白，本文提出论点优化任务：重写论证性论点以优化其表达。由于存在多种优化类型，我们通过首先生成一组多样化的候选论点（利用BART等大型语言模型，考虑上下文信息）来处理此任务。随后，基于多种质量指标选择最佳候选。在英语语料库的自动评估与人工评估中，我们基于质量的候选选择方法显著优于多个基线，改进了60%的论点（仅使16%恶化）。后续分析表明，除文本编辑外，我们的方法常通过细节具体化论点，但添加的证据少于人类。此外，该方法的泛化能力良好，可应用于其他领域，如教学文本。