The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
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DOI：
10.18653/v1/n18-1175
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发表时间：
2017-08
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通讯作者：
Ivan Habernal;Henning Wachsmuth;Iryna Gurevych;Benno Stein
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文献类型：
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作者：
Ivan Habernal;Henning Wachsmuth;Iryna Gurevych;Benno Stein
推理是自然语言论证的重要组成部分。要理解一个论证，就必须分析其论证理由，即为什么论证的主张是根据其前提得出的。由于论证是高度语境化的，论据通常是预先假定的，而且是隐含的。因此，理解论证不仅需要语言理解能力和逻辑技巧，还有赖于常识。在本文中，我们开发了一种系统重构权证的方法。我们在一个可扩展的众包过程中将其付诸实施，最终建立了一个免费授权的数据集，其中包含来自新闻评论的 2k 个真实论据的授权。在此基础上，我们提出了一项新的挑战性任务--论证推理理解任务。给定一个带有主张和前提的论证，目标是从两个选项中选择正确的隐含权证。这两个授权都是可信的，在词法上也很接近，但却会导致相互矛盾的主张。这项任务的解决将向自动权证重构迈出实质性的一步。然而，使用几种神经注意和语言模型进行的实验表明，目前的方法还远远不够。
Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.