Towards Causal Explanation Detection with Pyramid Salient-Aware Network

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 \emph{Causal Explanation Detection}, an important subtask of causal explanation analysis, which determines whether a causal explanation exists in one message. We design a \textbf{P}yramid \textbf{S}alient-\textbf{A}ware \textbf{N}etwork (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 \emph{Causal Explanation Detection} task.