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.

中文翻译：

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使用金字塔显着感知网络进行因果解释检测

因果解释分析（CEA）可以帮助我们理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，我们关注因果解释检测，这是因果解释分析的一个重要子任务，它确定一个消息中是否存在因果解释。我们设计了一个金字塔显着感知网络（PSAN）来检测消息的因果解释。PSAN 可以通过基于底部图的词级显着网络捕获关键字中包含的话语的显着语义来协助因果解释检测。此外，PSAN 可以通过基于顶级注意力的话语级显着网络来修改话语的主导地位，以增强消息的解释性语义。