Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory (1704.01074v4)

Abstract: Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.

Overview of "Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory"

The paper "Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory" introduces the Emotional Chatting Machine (ECM), which is a novel framework developed to generate responses in dialogue systems that are not only relevant and grammatically correct but also emotionally consistent. This paper is significant as it aims to bring emotional intelligence to conversational agents, a crucial aspect for enhancing user satisfaction and interaction quality.

Main Contributions

The paper presents several contributions to the field of natural language processing and dialogue systems:

1. Emotion Category Embedding: The framework incorporates emotion categories into the sequence-to-sequence (seq2seq) model by embedding emotion categories into a vector space. This allows the model to use high-level abstractions of emotions to influence the response generation dynamically.
2. Internal Emotion Memory: This mechanism helps in managing the dynamic nature of emotions during conversation by introducing an internal memory module. The internal memory decays the emotion state over time, ensuring that the emotional response is coherent and grammatically consistent throughout the generation process.
3. External Emotion Memory: This module serves to differentiate between generic words and emotion-specific words explicitly. By incorporating an external memory component, the model can make explicit choices to include emotion-laden words in the generated responses, thereby enhancing the emotional expressiveness of the responses.

Experimental Results

The effectiveness of ECM is demonstrated through several key metrics:

• Perplexity: ECM achieves a perplexity of 65.9, which is better than the baseline seq2seq model and comparable to the emotion category embedding model. This indicates that ECM can generate grammatically and contextually appropriate responses.
• Emotion Accuracy: ECM significantly outperforms both the seq2seq and the baseline model with an emotion accuracy of 0.773, showing its superior ability to generate emotionally appropriate responses.
• Manual Evaluation: Human judges rated the ECM's responses higher in both content and emotion criteria compared to the baselines. ECM responses scored 1.299 in content and 0.424 in emotion categories overall, indicating that the responses are more relevant and emotionally quantified accurately.

Implications and Future Directions

The incorporation of emotional intelligence into conversational agents has profound implications:

1. Enhanced User Experience: By generating emotionally appropriate responses, ECM can significantly improve user satisfaction and the perceived empathy of conversational agents.
2. Applications in Customer Service: Emotionally intelligent dialogue systems can be particularly beneficial in customer service scenarios where recognizing and responding to customer emotions can lead to better service outcomes and customer retention.
3. Theoretical Advancements: The mechanisms introduced in ECM could be applied to other areas of AI where emotional understanding and expression are crucial, such as in affective computing and human-computer interaction.

Future work could explore the automatic selection of emotion categories, enhancing the naturalness and contextual appropriateness of responses without needing pre-specified emotion categories. This requires further understanding of the interaction between conversational context, user mood, and response emotion. Additionally, addressing the scarcity of high-quality, emotion-labeled large-scale training data remains a significant challenge.

Conclusion

In conclusion, the paper makes a valuable contribution to the development of emotionally intelligent dialogue systems by introducing novel mechanisms for emotion expression in conversation generation. The successful integration of these mechanisms highlights the potential for future advancements in creating more engaging and empathetic artificial intelligence systems.