GraphNLI：一种基于图的自然语言推理模型用于在线辩论的极性预测
GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates
摘要：
允许用户之间参与的在线论坛对于重要问题的公开讨论具有变革性。然而，在这些论坛上的辩论有时会升级为全面的仇恨或错误信息交流。理解和处理这些问题的一个重要工具是能够推断出回复是支持还是攻击它所回复的帖子的争论关系。这种所谓的极性预测任务是困难的，因为回复可能基于帖子之外的外部上下文和其极性正被预测的回复。我们提出了GraphNLI，一种新的基于图的深度学习架构，它使用图行走技术以原则性的方式捕获讨论线程的更广泛的上下文。具体来说，我们提出了一些方法来执行从帖子开始的寻根图遍历，并捕获其周围的上下文，为帖子生成额外的嵌入。然后，我们使用这些嵌入来预测回复和它正在回复的帖子之间的极性关系。我们评估了我们的模型在来自在线辩论平台Kialo的策划辩论数据集上的性能。我们的模型优于包括S-BERT在内的相关基线，总体准确率为83%。
英文摘要：
Online forums that allow participatory engagement between users have been transformative for public discussion of important issues. However, debates on such forums can sometimes escalate into full blown exchanges of hate or misinformation. An important tool in understanding and tackling such problems is to be able to infer the argumentative relation of whether a reply is supporting or attacking the post it is replying to. This so called polarity prediction task is difficult because replies may be based on external context beyond a post and the reply whose polarity is being predicted. We propose GraphNLI, a novel graph-based deep learning architecture that uses graph walk techniques to capture the wider context of a discussion thread in a principled fashion. Specifically, we propose methods to perform root-seeking graph walks that start from a post and captures its surrounding context to generate additional embeddings for the post. We then use these embeddings to predict the polarity relation between a reply and the post it is replying to. We evaluate the performance of our models on a curated debate dataset from Kialo, an online debating platform. Our model outperforms relevant baselines, including S-BERT, with an overall accuracy of 83%.
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