JMI at SemEval 2024 Task 3: Two-step approach for multimodal ECAC using in-context learning with GPT and instruction-tuned Llama models

arXiv:2403.04798v1 Announce Type: cross
Abstract: 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, …

abstract analysis arxiv audio competition context conversations cs.cl cs.lg development emotion emotions gpt human in-context learning instruction-tuned llama llama models multimodal multiple paper text type video