activity
20192026
most citedBinarized Knowledge Graph Embeddings

2 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Generating Diverse Personas for User Simulators to Test Interview Dialogue Systems

Mikio Nakano, Kazunori Komatani, Hironori Takeuchi

This paper addresses the issue of the significant labor required to test interview dialogue systems. While interview dialogue systems are expected to be useful in various scenarios…

cs.HC2026

A Methodology for Identifying Evaluation Items for Practical Dialogue Systems Based on Business-Dialogue System Alignment Models

Mikio Nakano, Hironori Takeuchi, Kazunori Komatani

This paper proposes a methodology for identifying evaluation items for practical dialogue systems. Traditionally, user satisfaction and user experiences have been the primary metri…

cs.SE2025

Dialogue Systems Engineering: A Survey and Future Directions

Mikio Nakano, Hironori Takeuchi, Sadahiro Yoshikawa +2

This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also d…

cs.HC2023

User-adaptive Tourist Information Dialogue System with Yes/No Classifier and Sentiment Estimator

Ryo Yanagimoto, Yunosuke Kubo, Miki Oshio +3

We introduce our system developed for Dialogue Robot Competition 2023 (DRC2023). First, rule-based utterance selection and utterance generation using a large language model (LLM) a…

cs.HC2022

Team OS's System for Dialogue Robot Competition 2022

Yuki Kubo, Ryo Yanagimoto, Hayato Futase +3

This paper describes our dialogue robot system, OSbot, developed for Dialogue Robot Competition 2022. The dialogue flow is based on state transitions described manually and the tra…

cs.LG2019★ 2 cited

Binarized Knowledge Graph Embeddings

Koki Kishimoto, Katsuhiko Hayashi, Genki Akai +2

Tensor factorization has become an increasingly popular approach to knowledge graph completion(KGC), which is the task of automatically predicting missing facts in a knowledge grap…