论文标题
通过金字塔突出感知网络迈向因果解释检测
Towards Causal Explanation Detection with Pyramid Salient-Aware Network
论文作者
论文摘要
因果解释分析（CEA）可以帮助我们了解日常事件背后的原因，这对于理解信息的连贯性非常有帮助。在本文中，我们重点介绍因果解释检测，这是因果解释分析的重要子任务，该检测决定了一条消息中是否存在因果解释。我们设计了金字塔突出感知网络（PSAN），以检测有关消息的因果解释。 PSAN可以通过捕获基于底部的基于图的单词级别的明显网络中包含的话语的显着语义来帮助因果解释检测。此外，PSAN可以通过基于注意力的话语级别的突出网络来修改话语的主导地位，以增强消息的解释性语义。在CEA的常用数据集上的实验表明，在因果解释检测任务上，PSAN的表现优于最新方法的F1值。
Causal explanation analysis (CEA) can assist us to understand the reasons behind daily events, which has been found very helpful for understanding the coherence of messages. In this paper, we focus on Causal Explanation Detection, an important subtask of causal explanation analysis, which determines whether a causal explanation exists in one message. We design a Pyramid Salient-Aware Network (PSAN) to detect causal explanations on messages. PSAN can assist in causal explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network. Furthermore, PSAN can modify the dominance of discourses via a top attention-based discourse-level salient network to enhance explanatory semantics of messages. The experiments on the commonly used dataset of CEA shows that the PSAN outperforms the state-of-the-art method by 1.8% F1 value on the Causal Explanation Detection task.
