GuReT: Distinguishing Guilt and Regret related Text

GuReT: Distinguishing Guilt and Regret related Text
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Original Paper
 
Abstract:
The intricate relationship between human decision-making and emotions, particularly guilt and regret, has significant implications on behavior and well-being. Yet, these emotions subtle distinctions and interplay are often overlooked in computational models. This paper introduces a dataset tailored to dissect the relationship between guilt and regret and their unique textual markers, filling a notable gap in affective computing research. Our approach treats guilt and regret recognition as a binary classification task and employs three machine learning and six transformer-based deep learning techniques to benchmark the newly created dataset. The study further implements innovative reasoning methods like chain-of-thought and tree-of-thought to assess the models interpretive logic. The results indicate a clear performance edge for transformer-based models, achieving a 90.4% macro F1 score compared to the 85.3% scored by the best machine learning classifier, demonstrating their superior capability in distinguishing complex emotional states.
 

Summary Notes

GuReT: Enhancing Emotional AI by Understanding Guilt and Regret with NLP

Emotions significantly influence human decisions, and understanding these emotions, especially guilt and regret, is crucial for developing more sophisticated emotional AI.
This post discusses the distinctions between guilt and regret, the challenges they pose in natural language processing (NLP), and how the introduction of the GuReT dataset is changing the game for emotional AI development.

Understanding Guilt and Regret

  • Guilt is feeling responsible for a wrongdoing, often involving ethical or legal boundaries.
  • Regret is feeling sorry for actions or inactions, which could affect oneself or others.
Both emotions affect decision-making but are distinct in their nature and impact on well-being.

NLP Challenges

Identifying guilt and regret in text is tricky due to their nuanced language and context similarities. Accurate differentiation is essential for AI models designed to interpret or generate human-like text, presenting a significant challenge for AI Engineers.

Advancements in Emotional AI

The GuReT dataset is a breakthrough in emotion classification, enabling AI to distinguish between guilt and regret more accurately. It leverages machine learning, transformer-based models, and advanced NLP techniques such as chain-of-thought (CoT) and tree-of-thought (ToT) prompting, improving AI's reasoning capabilities.

Key Methods and Findings

  • Machine Learning Models have achieved an F1 score of up to 85.3%.
  • Transformer-Based Models have reached a higher F1 score of 90.4%, showcasing their efficiency.
  • The GuReT Dataset provides annotated texts to train AI in recognizing emotional cues, enhancing the precision of guilt and regret classification.

Experimental Insights

Comparative testing with the GuReT dataset demonstrates that transformer-based models outperform traditional machine learning in emotion classification, highlighting their potential in processing complex emotional states.

Challenges and Future Work

While progress has been made, distinguishing between closely related emotions remains challenging. Future efforts could expand the dataset, explore more emotions, and refine AI models to improve accuracy.

Wrap-Up

The exploration of guilt and regret in text through NLP and the GuReT dataset marks significant progress in emotional AI.
Transformer-based models, in particular, show promise for advancing AI's ability to understand and interact based on human emotions.
As we move forward, leveraging these advancements will be key to developing AI that can empathize and engage more deeply with human nuances.

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