Neural Argument Generation Augmented with Externally Retrieved Evidence
Neural Argument Generation Augmented with Externally Retrieved Evidence
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DOI：
10.18653/v1/p18-1021
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Xinyu Hua;Lu Wang-
中科院分区：
文献类型：
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作者：
Xinyu Hua;Lu Wang-
高质量的论证是人类推理和决策过程的基本要素。然而，有效的论证构建对于人类和机器来说都是一项具有挑战性的任务。在这项工作中，我们研究了一项新任务，即针对给定的陈述自动生成不同立场的论点。我们提出了一种编码器-解码器风格的基于神经网络的参数生成模型，该模型丰富了从维基百科外部检索的证据。我们的模型首先生成一组谈话要点短语作为中间表示，然后由一个单独的解码器根据输入和关键短语生成最终参数。对从 Reddit 收集的大规模数据集进行的实验表明，根据自动评估和人工评估，我们的模型比流行的序列到序列生成模型构建了具有更多主题相关内容的论点。
High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than popular sequence-to-sequence generation models according to automatic evaluation and human assessments.