Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914639848144896 |
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| author | DeLucia, Alexandra Zhao, Mengjie Maeda, Yoshinori Yoda, Makoto Yamada, Keiichi Wakaki, Hiromi |
| author_facet | DeLucia, Alexandra Zhao, Mengjie Maeda, Yoshinori Yoda, Makoto Yamada, Keiichi Wakaki, Hiromi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06742 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain DeLucia, Alexandra Zhao, Mengjie Maeda, Yoshinori Yoda, Makoto Yamada, Keiichi Wakaki, Hiromi Computation and Language Artificial Intelligence 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. |
| title | Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2401.06742 |