  In this paper, we present MasonTigers' participation in SemEval-2024 Task 10,
a shared task aimed at identifying emotions and understanding the rationale
behind their flips within monolingual English and Hindi-English code-mixed
dialogues. This task comprises three distinct subtasks - emotion recognition in
conversation for Hindi-English code-mixed dialogues, emotion flip reasoning for
Hindi-English code-mixed dialogues, and emotion flip reasoning for English
dialogues. Our team, MasonTigers, contributed to each subtask, focusing on
developing methods for accurate emotion recognition and reasoning. By
leveraging our approaches, we attained impressive F1-scores of 0.78 for the
first task and 0.79 for both the second and third tasks. This performance not
only underscores the effectiveness of our methods across different aspects of
the task but also secured us the top rank in the first and third subtasks, and
the 2nd rank in the second subtask. Through extensive experimentation and
analysis, we provide insights into our system's performance and contributions
to each subtask.
