Abstract
We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction. The network operates at the paragraph level and integrates convolutional and recurrent neural networks, as well as a similarity matrix as part of the overall architecture. The experimental evaluation on the Fake News Challenge dataset shows state-of-the-art performance.
Abstract (translated)
我们提出了一种用于立场检测的新型端到端记忆网络,它联合(i)预测文档是否与给定的目标声明相一致,不一致,讨论或无关,并且(ii)提取证据摘录那个预言。网络运行在段落级别,集成了卷积和递归神经网络,以及作为整体架构一部分的相似矩阵。 “假新闻挑战”数据集的实验评估显示了最先进的性能。
URL
https://arxiv.org/abs/1804.07581