Summary of Affective-nli: Towards Accurate and Interpretable Personality Recognition in Conversation, by Zhiyuan Wen et al.
Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation
by Zhiyuan Wen, Jiannong Cao, Yu Yang, Ruosong Yang, Shuaiqi Liu
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 In this paper, researchers propose a novel approach to Personality Recognition in Conversation (PRC), focusing on identifying speakers’ traits through textual dialogue content. The goal is to provide personalized services in HCI applications like AI-based mental therapy and companion robots for the elderly. Recent studies have analyzed dialog content for personality classification but overlooked crucial implicit factors, such as emotions, and semantic understanding of personality, reducing interpretability. Affective Natural Language Inference (Affective-NLI) is proposed to address these concerns. The approach fine-tunes a pre-trained language model for emotion recognition in conversations, facilitating real-time affective annotations for utterances. For interpretability, PRC is formulated as an NLI problem by determining whether personality labels are entailed by dialog content. Experiments on two daily conversation datasets show that Affective-NLI outperforms state-of-the-art approaches by 6%-7%, and the Flow experiment demonstrates accurate recognition in early conversation stages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Personality Recognition in Conversation (PRC) is a new way to understand people’s personalities through their conversations. This can help create personalized services for people, like AI-based therapy or robots that are friends to the elderly. Previous research has tried to recognize personalities from conversations, but they didn’t consider important things like emotions and how we understand personality itself. This makes it hard to understand why the results say what they do. The researchers in this paper came up with a new approach called Affective Natural Language Inference (Affective-NLI). It’s like training a language model to recognize emotions in conversations, which helps make the results more accurate and easy to understand. They tested their approach on two big datasets of daily conversations and found that it did much better than previous methods. |
Keywords
» Artificial intelligence » Classification » Inference » Language model
