Summary of Personality-affected Emotion Generation in Dialog Systems, by Zhiyuan Wen et al.
Personality-affected Emotion Generation in Dialog Systems
by Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Ruosong Yang, Shuaiqi Liu, Maosong Sun
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to generating emotions in dialog systems is proposed, focusing on personality-based emotional responses to enhance user engagement. The current methods rely on learning empathetic manners from anonymous conversational data, but this can lead to inconsistent emotional expressions, decreasing service quality. Psychological findings suggest that human emotional expressions are rooted in personality traits, prompting the introduction of a new task: Personality-affected Emotion Generation. This involves generating emotions based on the personality given to the dialog system and investigating mood transition processes. A daily dialog dataset, Personality EmotionLines Dataset (PELD), is constructed with emotion and personality annotations. The challenges in this task include integrating personality and emotional factors and extracting multi-granularity emotional information in the dialog context. To address these challenges, a method is proposed to model the personality as the transition weight by simulating the mood transition process in the dialog system. Experiments on PELD show that this approach improves emotion generation performance by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Emotions are important for making conversations feel natural and friendly. Right now, many chatbots try to understand how people feel, but they often struggle to express emotions that match the person’s personality. This can make the conversation feel fake or uninteresting. Scientists have found that humans’ emotional expressions are connected to their personalities, so they propose a new way of making chatbots generate emotions based on those personalities. To test this idea, they created a dataset called Personality EmotionLines Dataset (PELD) with conversations that include information about people’s emotions and personalities. They also came up with ways to address the challenges in this task, such as combining personality and emotional factors. By using their approach, chatbots can generate more accurate and personalized emotional responses. |
Keywords
» Artificial intelligence » Bert » Prompting
