Argument Generation with Retrieval, Planning, and Realization
Argument Generation with Retrieval, Planning, and Realization
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DOI：
10.18653/v1/p19-1255
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发表时间：
2019-06
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影响因子：
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通讯作者：
Xinyu Hua;Zhe Hu;Lu Wang
中科院分区：
文献类型：
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作者：
Xinyu Hua;Zhe Hu;Lu Wang
自动论据生成是一项吸引人但具有挑战性的任务。本文研究了反论证生成的具体问题，提出了一个新的框架——CANDELA。它包括一个强大的检索系统和一种新颖的两步生成模型，其中文本规划解码器首先决定每个句子的主要谈话要点和适当的语言风格，然后内容实现解码器反映这些决定并构建信息丰富的段落级论点。此外，我们的生成模型由检索系统授权，检索系统收集了来自维基百科和流行英语新闻媒体的1200万篇文章，提供了访问多样性的高质量内容。对从Reddit收集的大规模数据集的自动评估表明，我们的模型产生的BLEU、ROUGE和METEOR分数明显高于最先进的和非琐碎的比较。人的评价进一步表明，我们的系统论证更适合于反驳，内容更丰富。
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.