What is it about?
The authors propose adding an emotion detection module in the conversational agent to identify the emotions in user input. This emotion analysis will also help the conversational agent to respond emotionally. The objective of the work is to annotate the emotion label of user input in a dialogue between the conversational agent and the user.
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Why is it important?
This paper offers an overview of the practical implications of emotion detection techniques in conversational systems and their impact on user response. The outcomes of this paper contribute to the ongoing development of empathetic conversational agents, emphasizing natural human-machine interactions.
Perspectives
The authors can conclude that Artificial Intelligence has played a role in the considerable advancement of text-based emotion detection in recent years. The development of several novel concepts, including pre-trained embedding, various attention mechanisms, transformer-based models, and pretrained deep learning models, has led to significant growth in recent years.
Dr Ketan Kotecha
Symbiosis International University
Read the Original
This page is a summary of: Understanding the performance of AI algorithms in Text-Based Emotion Detection for Conversational Agents, ACM Transactions on Asian and Low-Resource Language Information Processing, January 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3643133.
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