Conditional Causal Relationships between Emotions and Causes in Texts

Xinhong Chen, Qing Li, Jianping Wang

Information Retrieval and Text Mining Long Paper

Gather-2I: Nov 17, Gather-2I: Nov 17 (10:00-12:00 UTC) [Join Gather Meeting]

You can open the pre-recorded video in a separate window.

Abstract: 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.
NOTE: Video may display a random order of authors. Correct author list is at the top of this page.

Connected Papers in EMNLP2020

Similar Papers