From Natural-Language Descriptions to OCEAN Representations for Dialogue Generation

Natural-language descriptions provide an intuitive way to specify conversational agent behavior, but they do not directly support systematic control. We introduce a two-stage framework that maps free-form character descriptions to continuous Five-Factor Model (OCEAN) representations and uses them to guide personality expression in dialogue. NaturalOCEAN combines retrieval-augmented language-model inference with post-hoc calibration to estimate Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Retrieval generally reduces uncalibrated prediction error, while calibration substantially improves alignment with reference OCEAN scores. OCEANDialogue learns five trait-specific LoRA components whose contributions are scaled by the corresponding OCEAN values. OCEANDialogue produces stronger and more monotonic graded control than numerical zero-shot prompting for Openness, Conscientiousness, and Extraversion. Cross-trait analysis indicates that the learned controls are only partially disentangled, while human and end-to-end evaluations show that intended personality differences are perceptible and that salient characteristics from the original descriptions remain recognizable in generated dialogue. The results support continuous OCEAN representations as an interpretable interface between natural-language character specification and controllable personality expression in dialogue.