IITK at S em E val-2024 Task 10: Who is the speaker? Improving Emotion Recognition and Flip Reasoning in Conversations via Speaker Embeddings (2024)

Abstract: This paper presents our approach for the SemEval-2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversations. We propose a transformer-based speaker-centric model for the Emotion Flip Reasoning (EFR) task and a masked-memory network along with a speaker participation vector for the Emotion Recognition in Conversations (ERC) task. We propose a Probable Trigger Zone, which is more likely to contain the utterances causing the emotion of a speaker to flip. In EFR, sub-task 3, the proposed approach archives a 5.9 (F1 score) improvement over the provided task baseline. The ablation study results highlight the significance of various design choices in the proposed method.

## introduction conversations between participants carry information that evokes emotions. emotions include personality, character, temper, and inspiration as the primary psychological parameters that drive them @xcite . analyzing emotions through language helps uncover the interpersonal sentiments in a conversation at a finer level. this can help build better affective generative models @xcite , like chatbots that understand emotion and respond according to a person's behaviors and personality @xcite . the semeval-2024 task 10 ( @xcite ) aims at emotion recognition (erc), sub-task 1, and emotion flip reasoning (efr), sub-tasks 2 and 3, in conversations for two languages, namely english and hindi-english code-mixed. erc refers to identifying the emotion of different utterances. efr is about identifying those utterances in the dialogue that caused the emotion of a speaker to change. ## system overview inspired by the use of memory networks by @xcite and @xcite for emotion recognition, in 4.2 we present our model for the task of erc, sub-task 1. inspired by a transformer-based @xcite approach for emotion flip reasoning presented by @xcite , in 4.3 we present our models for sub-tasks 2 and 3. ## results for sub-task 1, the dataset consisted of a nonuniform distribution of labels, with neutral being the most frequent. a model that predicts the emotion category of each utterance to be neutral achieves a weighted f1 of 24.36. we consider this as a simple neutral baseline for sub-task 1. for sub-task 2, we have kept the baseline as a rule-based model that predicts the previous utterance to be a trigger and the rest of all utterances non-triggers. the data for the second sub-task is highly skewed as can be seen in figure 1a . due to this baseline performs exceptionally well, as can be observed in rule-based: a rule-based model that predicts the previous utterance as a trigger and the rest as non-triggers. ## conclusion in this paper, we discussed approaches to improve the masked memory network architecture for emotion recognition (erc) and transformer-based architecture for emotion flip reasoning (efr) by incorporating speaker information into the embeddings and making better use of the emotion labels. we also employed an approach of focusing the limitations: our model assumes that the training and testing data consist of the same speakers. while this would be true for many benchmark datasets of emotion analysis in conversations, it might not be true in all real-world circumstances. another limitation of the proposed approach is the training time. future work: in the future, we can include speaker information across dialogues for erc to capture better speaker semantics by using learnable embeddings for each speaker updated by the hidden outputs of the speaker-level gru. however, to apply the above, we need to know the number of speakers in the datasets, training, and testing. additionally, the model becomes dependent on the number of speakers. a possible approach to help mitigate the assumption of having common speakers and knowing the number of speakers in the training and test time could be exploring further modeling inter and intraspeaker dependencies, as shown in @xcite and @xcite . they propose models that capture speaker relationships but are not dependent on the number of speakers. mitigating the issues of skewed data can be further explored to enhance the system's performance. also, addressing other aspects of conversations, such as whom the statement is being told to and treating names of speakers in utterances differently from simple pronouns, can be explored.