K-Act2Emo: Korean Commonsense Knowledge Graph for Indirect Emotional Expression

Core Concepts

K-Act2Emo introduces a specialized Korean commonsense knowledge graph focusing on indirect emotional expressions, enhancing emotion inference models' training effectiveness.

Abstract

Abstract: Emotions in literary texts are conveyed indirectly through actions and appearances. K-Act2Emo is a Korean CSKG with 1,900 emotional expressions and inferences. Reasoning types categorized into positive, negative situations, and non-emotional cues. Introduction: Literary texts convey emotions indirectly through nonverbal cues. Existing datasets lack comprehensive coverage of indirect emotional expressions. K-Act2Emo focuses on indirect emotional expressions in Korean literature. Collection: Two-step process for gathering indirect emotional expressions through crowdsourcing. Classification of reasoning types into positive, negative situations, and non-emotional cues. Statistics: Acceptance rate of nodes in the dataset evaluated by crowdsourcing participants. Distribution of unique Tails across different reasoning types. Training Details: Fine-tuning COMET-BART model with K-Act2Emo dataset for emotion inference tasks. Results: COMET-BART outperforms other models in automatic evaluations for emotion inference tasks. Human evaluations show consistent performance differences among models. Discussion: Limitations in distinguishing emotions with similar arousal levels. Importance of differentiating between subjective viewpoints and objective descriptions.

Stats

In constructing K-Act2Emo, we employ a two-step process. First, we gather indirect emotional expressions through crowdsourcing. Then, we collect corresponding emotions and other inferences through a second round of crowdsourcing. The acceptance rate for nodes in NegEnv is slightly lower compared to that in PosEnv. The evaluation results indicate an acceptance rate of 82.53% for the dataset.

Quotes

"In this study, we compare K-Act2Emo with ATOMIC20 20." "Kullm We used a 5.8B-sized version of Kullm based on polyglot-ko."

Key Insights Distilled From

K-Act2Emo

by Kyuhee Kim,S... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14253.pdf

Deeper Inquiries

How can the taxonomy of reasoning types be further refined to address nuances in emotional inference?

The taxonomy of reasoning types can be enhanced by incorporating additional subcategories that capture more nuanced aspects of emotional inference. For example, introducing categories that consider the intensity or duration of emotions expressed indirectly could provide a deeper understanding of the emotional context. Moreover, including a category for ambiguous expressions that may convey multiple emotions simultaneously would help in refining the taxonomy. By refining the taxonomy to account for these nuances, we can improve the accuracy and granularity of emotional inference models.

What implications does the observer's perspective have on interpreting indirect emotional expressions?

The observer's perspective plays a crucial role in interpreting indirect emotional expressions as it influences how actions, facial expressions, and gestures are perceived and understood. Different observers may interpret the same expression differently based on their own experiences, beliefs, and cultural background. This subjectivity introduces biases into the interpretation process and highlights the importance of considering multiple perspectives when analyzing indirect emotional cues. Understanding how an observer's viewpoint shapes their interpretation is essential for accurately deciphering complex emotional narratives conveyed through nonverbal cues.

How can the level of volition in actions impact the accuracy of emotional inference models?

The level of volition in actions directly impacts the accuracy of emotional inference models by influencing how emotions are attributed to specific behaviors or gestures. Actions driven by conscious intent may not always align with underlying emotions, leading to potential misinterpretations if not carefully considered. For instance, voluntary actions performed out of social norms or expectations might not reflect true internal states accurately. By acknowledging and accounting for varying levels of volition in actions within emotional inference models, we can enhance their ability to discern genuine emotions from intentional displays or societal conventions effectively.