Representing and Determining Argumentative Relevance in Online Discussions: A General Approach
Representing and Determining Argumentative Relevance in Online Discussions: A General Approach
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DOI：
10.1609/icwsm.v17i1.22146
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Zhen Guo;Munindar P. Singh
中科院分区：
文献类型：
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作者：
Zhen Guo;Munindar P. Singh
了解在线辩论讨论对于了解用户对主题的意见及其潜在推理至关重要。确定辩论性讨论的完整性和说服力的一个关键挑战是评估一个主题下的论点如何以逻辑和连贯的方式联系起来。与论文或面对面交流相比，在线辩论讨论对判断论点相关性的技术提出了挑战，因为在线讨论涉及多个参与者，并且经常表现出推理的不连贯性和写作风格的不一致性。我们将相关性定义为在线讨论中代表论点片段的小文本之间的逻辑和主题联系。我们提供了一个语料库，包括成对的句子，标记与每对句子之间的议论性相关性。我们提出了一种计算方法，依赖于内容减少和连体神经网络架构的建模论证连接和确定文本之间的论证相关性。实验结果表明，我们的方法是有效的测量参数之间的相关性，并优于强大的和良好的采用基线。进一步的分析表明，在下游任务中使用我们的论证相关性编码的好处，预测在线评论对特定主题的影响力，与不考虑逻辑连接的编码相比。
Understanding an online argumentative discussion is essential for understanding users' opinions on a topic and their underlying reasoning. A key challenge in determining completeness and persuasiveness of argumentative discussions is to assess how arguments under a topic are connected in a logical and coherent manner. Online argumentative discussions, in contrast to essays or face-to-face communication, challenge techniques for judging argument relevance because online discussions involve multiple participants and often exhibit incoherence in reasoning and inconsistencies in writing style. We define relevance as the logical and topical connections between small texts representing argument fragments in online discussions. We provide a corpus comprising pairs of sentences, labeled with argumentative relevance between the sentences in each pair. We propose a computational approach relying on content reduction and a Siamese neural network architecture for modeling argumentative connections and determining argumentative relevance between texts. Experimental results indicate that our approach is effective in measuring relevance between arguments, and outperforms strong and well-adopted baselines. Further analysis demonstrates the benefit of using our argumentative relevance encoding on a downstream task, predicting how impactful an online comment is to certain topic, comparing to encoding that does not consider logical connection.