  This paper presents our system development for SemEval-2024 Task 3: "The
Competition of Multimodal Emotion Cause Analysis in Conversations". Effectively
capturing emotions in human conversations requires integrating multiple
modalities such as text, audio, and video. However, the complexities of these
diverse modalities pose challenges for developing an efficient multimodal
emotion cause analysis (ECA) system. Our proposed approach addresses these
challenges by a two-step framework. We adopt two different approaches in our
implementation. In Approach 1, we employ instruction-tuning with two separate
Llama 2 models for emotion and cause prediction. In Approach 2, we use GPT-4V
for conversation-level video description and employ in-context learning with
annotated conversation using GPT 3.5. Our system wins rank 4, and system
ablation experiments demonstrate that our proposed solutions achieve
significant performance gains. All the experimental codes are available on
Github.
