  This paper describes our participation in SemEval 2024 Task 3, which focused
on Multimodal Emotion Cause Analysis in Conversations. We developed an early
prototype for an end-to-end system that uses graph-based methods from
dependency parsing to identify causal emotion relations in multi-party
conversations. Our model comprises a neural transformer-based encoder for
contextualizing multimodal conversation data and a graph-based decoder for
generating the adjacency matrix scores of the causal graph. We ranked 7th out
of 15 valid and official submissions for Subtask 1, using textual inputs only.
We also discuss our participation in Subtask 2 during post-evaluation using
multi-modal inputs.
