SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model
This addresses emotion recognition in conversations for applications like chatbots, but it is incremental as it builds on existing speaker relationship modeling.
The paper tackled conversation emotion recognition by modeling utterance emotional tendency with sentence-level emotion orientation vectors, resulting in better performance on two benchmark datasets compared to five baseline models.
For the task of conversation emotion recognition, recent works focus on speaker relationship modeling but ignore the role of utterance's emotional tendency.In this paper, we propose a new expression paradigm of sentence-level emotion orientation vector to model the potential correlation of emotions between sentence vectors. Based on it, we design an emotion recognition model, which extracts the sentence-level emotion orientation vectors from the language model and jointly learns from the dialogue sentiment analysis model and extracted sentence-level emotion orientation vectors to identify the speaker's emotional orientation during the conversation. We conduct experiments on two benchmark datasets and compare them with the five baseline models.The experimental results show that our model has better performance on all data sets.