MIME: MIMicking Emotions for Empathetic Response Generation (2010.01454v1)

Abstract: Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of this polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at https://github.com/declare-lab/MIME.

MIME: MIMicking Emotions for Empathetic Response Generation

The paper "MIME: MIMicking Emotions for Empathetic Response Generation" addresses key challenges in computational modeling of empathy within the domain of dialogue systems. Traditional approaches to empathetic response generation often view emotions in input texts as a uniform set, treating them with a flat hierarchical structure. The authors argue that understanding and emulating the intensity and polarity of these emotions—which vary in positivity and negativity—can significantly enhance the empathy and relevance of generated responses.

Core Contributions

The paper outlines two major contributions:

1. Emotion Mimicry Approach: The authors propose an innovative method that balances the mimicry of the user's emotions to generate more contextually appropriate responses. This approach involves classifying emotions into polarity-based clusters—positive and negative—to drive response generation effectively.
2. Introduction of Stochastic Emotion Mixtures: Inspired by the Mixture of Empathetic Listeners (MoEL) model, the paper introduces stochastic processes in the emotion mixtures. This adds variability in responses, allowing the model to generate diverse replies that can respond to a range of emotional contexts, including ambivalent situations.

Methodological Overview

MIME leverages emotion grouping to drive empathetic response generation, combining it with variational mechanisms to introduce diversity in emotional responses. This involves:

• Context Encoding: Using a transformer-based encoder to capture the emotional nuances and structural patterns within input dialogues.
• Response Generation: The authors use separate positive and negative emotion groups to guide response generation. These are combined using a gating mechanism that determines the appropriate mix of emotion influences for generating the final empathetic output.

Experimental Evaluation

MIME was evaluated using the EmpatheticDialogues dataset, demonstrating superior performance in both empathy and relevance compared to existing state-of-the-art models, including MoEL. Human evaluations reflected improvements in the empathetic quality and contextual relevance of conversations generated by MIME.

Implications and Future Directions

The introduction of stochastic emotion mixtures and emotion mimicry provides a new lens for understanding emotional depth in conversations, potentially enhancing AI's ability to participate in complex emotional dialogues. Practically, MIME can be integrated into systems where nuanced human-AI interactions are crucial, such as virtual therapy assistants or customer support.

From a theoretical perspective, this approach underlines the importance of nuanced emotional understanding in dialogue systems and sets the stage for future research into how models can better interpret and generate responses based on complex human emotions.

The paper opens avenues for further exploration into dynamically adjusting emotional strategies in dialogue models, emphasizing robust emotion classification and the synthesis of empathetic responses that align closely with human emotional patterns. This work may pave the way towards more adaptive and emotionally intelligent AI systems.