Towards Causal Explanation Detection with Pyramid Salient-Aware Network
This work addresses causal explanation detection, a subtask of causal explanation analysis, for applications in understanding message coherence, but it is incremental as it builds on existing methods with a modest performance gain.
The paper tackles the problem of detecting causal explanations in messages by proposing a Pyramid Salient-Aware Network (PSAN), which improves F1 score by 1.8% over the state-of-the-art on a standard dataset.
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.