Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain

• URL: http://arxiv.org/abs/2401.06742v1
• Date: Fri, 12 Jan 2024 18:25:03 GMT
• Title: Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain
• Authors: Alexandra DeLucia, Mengjie Zhao, Yoshinori Maeda, Makoto Yoda, Keiichi Yamada, Hiromi Wakaki
• Abstract summary: We introduce a natural language inference method for adapting a trained persona extraction model to a new setting. Our method returns higher-quality extracted persona and requires less human annotation.
• Score: 44.05974724495336
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While valuable datasets such as PersonaChat provide a foundation for training persona-grounded dialogue agents, they lack diversity in conversational and narrative settings, primarily existing in the "real" world. To develop dialogue agents with unique personas, models are trained to converse given a specific persona, but hand-crafting these persona can be time-consuming, thus methods exist to automatically extract persona information from existing character-specific dialogue. However, these persona-extraction models are also trained on datasets derived from PersonaChat and struggle to provide high-quality persona information from conversational settings that do not take place in the real world, such as the fantasy-focused dataset, LIGHT. Creating new data to train models on a specific setting is human-intensive, thus prohibitively expensive. To address both these issues, we introduce a natural language inference method for post-hoc adapting a trained persona extraction model to a new setting. We draw inspiration from the literature of dialog natural language inference (NLI), and devise NLI-reranking methods to extract structured persona information from dialogue. Compared to existing persona extraction models, our method returns higher-quality extracted persona and requires less human annotation.

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