  Conversation is the most natural form of human communication, where each
utterance can range over a variety of possible emotions. While significant work
has been done towards the detection of emotions in text, relatively little work
has been done towards finding the cause of the said emotions, especially in
multimodal settings. SemEval 2024 introduces the task of Multimodal Emotion
Cause Analysis in Conversations, which aims to extract emotions reflected in
individual utterances in a conversation involving multiple modalities (textual,
audio, and visual modalities) along with the corresponding utterances that were
the cause for the emotion. In this paper, we propose models that tackle this
task as an utterance labeling and a sequence labeling problem and perform a
comparative study of these models, involving baselines using different
encoders, using BiLSTM for adding contextual information of the conversation,
and finally adding a CRF layer to try to model the inter-dependencies between
adjacent utterances more effectively. In the official leaderboard for the task,
our architecture was ranked 8th, achieving an F1-score of 0.1759 on the
leaderboard.
