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.

Large Language Models (LLMs) have showcased impressive abilities in generating fluent responses to diverse user queries. However, concerns regarding the potential misuse ofsuch texts in journalism, educational, and academic contexts have surfaced. SemEval 2024introduces the task of Multigenerator, Multidomain, and Multilingual Black-Box MachineGenerated Text Detection, aiming to developautomated systems for identifying machinegenerated text and detecting potential misuse. In this paper, we i) propose a RoBERTaBiLSTM based classifier designed to classifytext into two categories: AI-generated or human ii) conduct a comparative study of ourmodel with baseline approaches to evaluate itseffectiveness. This paper contributes to the advancement of automatic text detection systemsin addressing the challenges posed by machinegenerated text misuse. Our architecture ranked46th on the official leaderboard with an accuracy of 80.83 among 125.