Abstract: To obtain a better understanding of user preferences in providing tailored services, dialogue systems have to generate semi-structured interviews that require flexible dialogue control while following a topic guide to accomplish the purpose of the interview. Toward this goal, this study proposes a semantics-aware GPT-3 fine-tuning model that generates interviews to acquire users' food preferences. The model was trained using dialogue history and semantic representation constructed from the communicative function and semantic content of the utterance. Using two baseline models: zero-shot ChatGPT and fine-tuned GPT-3, we conducted a user study for subjective evaluations alongside automatic objective evaluations. In the user study, in impression rating, the outputs of the proposed model were superior to those of baseline models and comparable to real human interviews in terms of eliciting the interviewees' food preferences.