Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation (2024)

Abstract: Emotion Recognition in Conversation (ERC) involves detecting the underlying emotion behind each utterance within a conversation. Effectively generating representations for utterances remains a significant challenge in this task. Recent works propose various models to address this issue, but they still struggle with differentiating similar emotions such as excitement and happiness. To alleviate this problem, We propose an Emotion-Anchored Contrastive Learning (EACL) framework that can generate more distinguishable utterance representations for similar emotions. To achieve this, we utilize label encodings as anchors to guide the learning of utterance representations and design an auxiliary loss to ensure the effective separation of anchors for similar emotions. Moreover, an additional adaptation process is proposed to adapt anchors to serve as effective classifiers to improve classification performance. Across extensive experiments, our proposed EACL achieves state-of-the-art emotion recognition performance and exhibits superior performance on similar emotions. Our code is available at https://github.com/Yu-Fangxu/EACL.

## introduction emotion recognition in conversation (erc) aims to identify the emotions of each utterance in a conversation. it plays an important role in various scenarios, such as chatbots, healthcare applications, and opinion mining on social media. however, the erc task faces several challenges. depending on the context, similar statements may exhibit entirely different emotional attributes. simultaneously, distinguishing conversation texts that contain similar emotional attributes is also extremely difficult @xcite . figure 1 is an example of a chat between a man and a woman. differentiating between happy and excited can be * corresponding author. well you paint a romantic picture. well i didn't want it to be like cheesy, you know. ## conclusion and future work this paper introduces a novel framework for conversational emotion recognition called emotionanchored contrastive learning. the proposed eacl leverages emotion representations as anchors to enhance the learning process of distinctive utterance representations. building upon this foundation, we further adapt the emotion anchors through finetuning, bringing them the optimal positions and more suitable for classification purposes. through extensive experiments and evaluations on three popular benchmark datasets, our approach achieves a new state-of-the-art performance. ablation studies and evaluations confirm that the proposed eacl framework significantly benefits dialogue modeling and enhances the learning of utterance representations for more accurate emotion recognition. the proposed eacl distributes the utterances in representation space more uniformly, which is beneficial for multi-class erc tasks. when considering the context of multi-label classification, eacl can group relevant emotions guided by human knowledge, or adjust the inter-class weights of contrastive losses with label similarity @xcite 2022; @xcite . then, eacl can serve to detect multiple emotions in a single utterance, which will be left for future work.