2024 Volume 39 Issue 3 Pages IDS6-A_1-15
Obtaining user preferences facilitates a better understanding of users to provide them with customized services. This paper proposes an interviewer response generation model for eliciting users’ food preferences. We collected 118 text-based dialogues that an interviewer asked the interviewee concerning their food preferences. We then assessed the responses to elicit detailed preference information, and represented the intention (communicative function) and meaning of these responses (semantic content) associated with objects, constructed from information pertaining to them, such as the names and ingredients of dishes, and their attributes, such as taste or cooking method. We created a GPT-3-based model which simultaneously generates the communication function, semantic content, and response sentences from the dialogue history, through the application of fine-tuning techniques. We investigated the performance of the proposed model by comparing it with the ground-truth interviewer utterances, Zero-shot ChatGPT, and a fine-tuned GPT-3 model that directly generates only response sentences as baselines. A user study evaluating the impression of the response sentences using a questionnaire showed that, in terms of eliciting interviewee food preferences, the proposed model’s response sentences were superior to those of the baseline models and comparable to real human interviews. These results are attributed to the proposed model’s frequent generation of questions, contributing to information extraction across various conversational contexts. We further found that, in comparison to ChatGPT, the questions generated by the proposed model are characterized by detailed questions concerning the words and content mentioned in or associated with the conversation context.
