Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions

• URL: http://arxiv.org/abs/2311.16579v1
• Date: Tue, 28 Nov 2023 07:47:25 GMT
• Title: Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions
• Authors: Xinhong Chen, Zongxi Li, Yaowei Wang, Haoran Xie, Jianping Wang, Qing Li
• Abstract summary: We propose a new task to determine whether an input pair of emotion and cause has a valid causal relationship under different contexts. We use negative sampling to construct the final dataset to balance the number of documents with and without causal relationships.
• Score: 37.16991100831717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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