Summary of Exploiting Emotion-semantic Correlations For Empathetic Response Generation, by Zhou Yang et al.
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
by Zhou Yang, Zhaochun Ren, Yufeng Wang, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Yunbing Wu, Yisong Su, Sibo Ju, Xiangwen Liao
First submitted to arxiv on: 27 Feb 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 This paper proposes a novel approach to generate empathetic responses by understanding the speaker’s emotional feelings from dialogue language. Current methods rely on static vectors that capture emotional words, neglecting the dynamic nature of emotions in language and their correlations with semantic roles. To address this issue, the authors introduce the Emotion-Semantic Correlation Model (ESCM), which constructs dynamic emotion-semantic vectors through context-emotion interactions and incorporates dependency trees to reflect these correlations. The ESCM is then used to generate empathetic responses on the EMPATHETIC-DIALOGUES dataset, achieving more accurate semantic and emotional understanding and producing fluent, informative responses. This work highlights the significance of considering both emotions and semantics in dialogue generation, which has implications for empathetic perception and expression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to understand and respond to people’s emotions in conversations. Current methods don’t take into account how emotions change over time or how they relate to other words in the conversation. The authors introduce a new model that considers these factors, called ESCM. This model helps the computer better understand emotional language and generate responses that are empathetic and informative. They tested this model on a dataset of conversations and found it worked well. This research is important because it can help computers better understand people’s emotions and respond in a way that feels more human-like. |
Keywords
» Artificial intelligence » Semantics
