Yukiko I. Nakano

Also published as: Yukiko Nakano


pdf bib
Question Generation to Elicit Users’ Food Preferences by Considering the Semantic Content
Jie Zeng | Yukiko Nakano | Tatsuya Sakato
Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue

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.


pdf bib
Semantic Content Prediction for Generating Interviewing Dialogues to Elicit Users’ Food Preferences
Jie Zeng | Tatsuya Sakato | Yukiko Nakano
Proceedings of the Second Workshop on When Creative AI Meets Conversational AI

Dialogue systems that aim to acquire user models through interactions with users need to have interviewing functionality. In this study, we propose a method to generate interview dialogues to build a dialogue system that acquires user preferences for food. First, we collected 118 text-based dialogues between the interviewer and customer and annotated the communicative function and semantic content of the utterances. Next, using the corpus as training data, we created a classification model for the communicative function of the interviewer’s next utterance and a generative model that predicts the semantic content of the utterance based on the dialogue history. By representing semantic content as a sequence of tokens, we evaluated the semantic content prediction model using BLEU. The results demonstrated that the semantic content produced by the proposed method was closer to the ground truth than the semantic content transformed from the output text generated by the retrieval model and GPT-2. Further, we present some examples of dialogue generation by applying model outputs to template-based sentence generation.


pdf bib
Predicting Evidence of Understanding by Monitoring User’s Task Manipulation in Multimodal Conversations
Yukiko Nakano | Kazuyoshi Murata | Mika Enomoto | Yoshiko Arimoto | Yasuhiro Asa | Hirohiko Sagawa
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions


pdf bib
Converting Text into Agent Animations: Assigning Gestures to Text
Yukiko I. Nakano | Masashi Okamoto | Daisuke Kawahara | Qing Li | Toyoaki Nishida
Proceedings of HLT-NAACL 2004: Short Papers


pdf bib
Towards a Model of Face-to-Face Grounding
Yukiko Nakano | Gabe Reinstein | Tom Stocky | Justine Cassell
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics


pdf bib
Non-Verbal Cues for Discourse Structure
Justine Cassell | Yukiko Nakano | Timothy W. Bickmore | Candace L. Sidner | Charles Rich
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics


pdf bib
Taking Account of the User’s View in 3D Multimodal Instruction Dialogue
Yukiko I. Nakano | Kenji Imamura | Hisashi Ohara
COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics


pdf bib
Cue Phrase Selection in Instruction Dialogue Using Machine Learning
Yukiko I. Nakano | Tsuneaki Kato
Discourse Relations and Discourse Markers


pdf bib
Towards Generation of Fluent Referring Action in Multimodal Situations
Tsuneaki Kato | Yukiko I. Nakano
Referring Phenomena in a Multimedia Context and their Computational Treatment