IITK at SemEval-2024 Task 10: Who is the speaker? Improving Emotion Recognition and Flip Reasoning in Conversations via Speaker Embeddings
This work addresses emotion analysis in conversational AI, providing incremental improvements for tasks like sentiment tracking and dialogue systems.
The paper tackled emotion recognition and flip reasoning in conversations by proposing a speaker-centric model and a Probable Trigger Zone concept, achieving a 5.9 F1 score improvement over the baseline for the flip reasoning sub-task.
This paper presents our approach for the SemEval-2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversations. For the Emotion Recognition in Conversations (ERC) task, we utilize a masked-memory network along with speaker participation. We propose a transformer-based speaker-centric model for the Emotion Flip Reasoning (EFR) task. We also introduce Probable Trigger Zone, a region of the conversation that is more likely to contain the utterances causing the emotion to flip. For sub-task 3, the proposed approach achieves a 5.9 (F1 score) improvement over the task baseline. The ablation study results highlight the significance of various design choices in the proposed method.