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The study of causal relationships between emotions and causes in texts hasrecently received much attention. Most works focus on extracting causallyrelated clauses from documents. However, none of these works has consideredthat the causal relationships among the extracted emotion and cause clauses canonly be valid under some specific context clauses. To highlight the context insuch special causal relationships, we propose a new task to determine whetheror not an input pair of emotion and cause has a valid causal relationship underdifferent contexts and extract the specific context clauses that participate inthe causal relationship. Since the task is new for which no existing dataset isavailable, we conduct manual annotation on a benchmark dataset to obtain thelabels for our tasks and the annotations of each context clause's type that canalso be used in some other applications. We adopt negative sampling toconstruct the final dataset to balance the number of documents with and withoutcausal relationships. Based on the constructed dataset, we propose anend-to-end multi-task framework, where we design two novel and general modulesto handle the two goals of our task. Specifically, we propose a context maskingmodule to extract the context clauses participating in the causalrelationships. We propose a prediction aggregation module to fine-tune theprediction results according to whether the input emotion and causes depend onspecific context clauses. Results of extensive comparative experiments andablation studies demonstrate the effectiveness and generality of our proposedframework.

Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions