The study of causal relationships between emotions and causes in texts has
recently received much attention. Most works focus on extracting causally
related clauses from documents. However, none of these works has considered
that the causal relationships among the extracted emotion and cause clauses can
only be valid under some specific context clauses. To highlight the context in
such special causal relationships, we propose a new task to determine whether
or not an input pair of emotion and cause has a valid causal relationship under
different contexts and extract the specific context clauses that participate in
the causal relationship. Since the task is new for which no existing dataset is
available, we conduct manual annotation on a benchmark dataset to obtain the
labels for our tasks and the annotations of each context clause's type that can
also be used in some other applications. We adopt negative sampling to
construct the final dataset to balance the number of documents with and without
causal relationships. Based on the constructed dataset, we propose an
end-to-end multi-task framework, where we design two novel and general modules
to handle the two goals of our task. Specifically, we propose a context masking
module to extract the context clauses participating in the causal
relationships. We propose a prediction aggregation module to fine-tune the
prediction results according to whether the input emotion and causes depend on
specific context clauses. Results of extensive comparative experiments and
ablation studies demonstrate the effectiveness and generality of our proposed
framework.

确定是否在不同的上下文范围内表达出的情绪和原因具有有效的因果关系，并提取参与因果关系的特定上下文从而构建一个可用于平衡具有和没有因果关系的文档数量的最终数据集，通过提出上下文屏蔽模块来提取参与因果关系的上下文子句，并通过提出预测聚合模块根据输入的情绪和原因是否依赖于特定的上下文子句来调整预测结果，验证了我们提出的框架的有效性和普遍性。