Using Natural Language Inference to Improve Persona Extraction from
  Dialogue in a New Domain
Alexandra DeLucia Mengjie Zhao Yoshinori Maeda Makoto Yoda Keiichi Yamada Hiromi Wakaki
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.