The causal relationships between emotions and causes in text have recently received a lot of attention. Most of the existing works focus on the extraction of the causally related clauses from documents. However, none of these works has considered the possibility that the causal relationships among the extracted emotion and cause clauses may only be valid under a specific context, without which the extracted clauses may not be causally related. To address such an issue, we propose a new task of determining whether or not an input pair of emotion and cause has a valid causal relationship under different contexts, and construct a corresponding dataset via manual annotation and negative sampling based on an existing benchmark dataset. Furthermore, we propose a prediction aggregation module with low computational overhead to fine-tune the prediction results based on the characteristics of the input clauses. Experiments demonstrate the effectiveness and generality of our aggregation module.
|Title of host publication||Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)|
|Place of Publication||Virtual (Online)|
|Publisher||Association for Computational Linguistics, ACL Anthology|
|Number of pages||11|
|Publication status||Published - 16 Nov 2020|