Emotion Cause Extraction (ECE) aims to reveal the cause clauses behind a given emotion expressed in a text, which has become an emerging topic in broad research communities, such as affective computing and natural language processing. Despite the fact that current methods about the ECE task have made great progress in text semantic understanding from lexicon- and sentence-level, they always ignore the certain causal narratives of emotion text. Significantly, these causal narratives are presented in the form of semantic structure and highly helpful for structure-level emotion cause understanding. Nevertheless, causal narrative is just an abstract narratological concept and its involving semantics is quite different from the common sequential information. Thus, how to properly model and utilize such particular narrative information to boost the ECE performance still remains an unresolved challenge. To this end, in this paper, we propose a novel Causal Narrative Comprehension Model (CNCM) for emotion cause extraction, which learns and leverages causal narrative information smartly to address the above problem. Specifically, we develop a Narrative-aware Causal Association (NCA) unit, which mines the narrative cue about emotional results and uses the semantic correlation between causes and results to model causal narratives of documents. Besides, we design a Result-aware Emotion Attention (REA) unit to make full use of the known result of causal narrative for multiple understanding about emotional causal associations. Through the ingenious combination and collaborative utilization of these two units, we could better identify the emotion cause in the text with causal narrative comprehension. Extensive experiments on the public English and Chinese benchmark datasets of ECE task have validated the effectiveness of CNCM with significant margin by comparing with the state-of-the-art baselines, which demonstrates the potential of narrative information in long text understanding.

中文翻译：

---

因果叙事理解：情感原因提取的新视角


情感原因提取（ECE）旨在揭示文本中表达的给定情感背后的原因条款，这已成为情感计算和自然语言处理等广泛研究领域的一个新兴课题。尽管当前的 ECE 任务方法在词汇和句子层面的文本语义理解方面取得了很大进展，但它们总是忽略情感文本的某些因果叙述。值得注意的是，这些因果叙述以语义结构的形式呈现，对于结构层面的情感原因理解非常有帮助。然而，因果叙事只是一个抽象的叙事学概念，其所涉及的语义与常见的顺序信息有很大不同。因此，如何正确建模和利用此类特定的叙述信息来提高 ECE 性能仍然是一个尚未解决的挑战。为此，在本文中，我们提出了一种用于情感原因提取的新颖的因果叙事理解模型（CNCM），该模型巧妙地学习和利用因果叙事信息来解决上述问题。具体来说，我们开发了一个叙事感知因果关联（NCA）单元，它挖掘有关情感结果的叙事线索，并使用原因和结果之间的语义相关性来建模文档的因果叙事。此外，我们设计了一个结果感知的情感注意（REA）单元，以充分利用因果叙述的已知结果来对情感因果关联进行多重理解。通过这两个单元的巧妙结合和协同利用，我们可以通过因果叙事理解更好地识别文本中的情感原因。 在 ECE 任务的公共英语和汉语基准数据集上进行的大量实验通过与最先进的基线进行比较，验证了 CNCM 的有效性，这证明了叙事信息在长文本理解中的潜力。