Search Results for author: Ryuichiro Higashinaka

Found 39 papers, 4 papers with code

Collection and Analysis of Travel Agency Task Dialogues with Age-Diverse Speakers

no code implementations LREC 2022 Michimasa Inaba, Yuya Chiba, Ryuichiro Higashinaka, Kazunori Komatani, Yusuke Miyao, Takayuki Nagai

This paper provides details of the dialogue task, the collection procedure and annotations, and the analysis on the characteristics of the dialogues and facial expressions focusing on the age of the speakers.

Dialogue Collection for Recording the Process of Building Common Ground in a Collaborative Task

no code implementations LREC 2022 Koh Mitsuda, Ryuichiro Higashinaka, Yuhei Oga, Sen Yoshida

To develop a dialogue system that can build common ground with users, the process of building common ground through dialogue needs to be clarified.

Data Collection for Empirically Determining the Necessary Information for Smooth Handover in Dialogue

no code implementations LREC 2022 Sanae Yamashita, Ryuichiro Higashinaka

In this study, we conducted a data collection experiment in which one of two operators talked to a user and switched with the other operator periodically while exchanging notes when the handovers took place.

Analysis of Dialogue in Human-Human Collaboration in Minecraft

no code implementations LREC 2022 Takuma Ichikawa, Ryuichiro Higashinaka

We also collected third-person evaluations of the gardens and analyzed the relationship between dialogue and collaborative work that received high scores on the subjective and third-person evaluations in order to identify dialogic factors for high-quality collaborative work.

A Speculative and Tentative Common Ground Handling for Efficient Composition of Uncertain Dialogue

no code implementations LREC 2022 Saki Sudo, Kyoshiro Asano, Koh Mitsuda, Ryuichiro Higashinaka, Yugo Takeuchi

This study investigates how the grounding process is composed and explores new interaction approaches that adapt to human cognitive processes that have not yet been significantly studied.

Team Flow at DRC2022: Pipeline System for Travel Destination Recommendation Task in Spoken Dialogue

no code implementations18 Oct 2022 Ryu Hirai, Atsumoto Ohashi, Ao Guo, Hideki Shiroma, Xulin Zhou, Yukihiko Tone, Shinya Iizuka, Ryuichiro Higashinaka

After the preliminary round of the competition, we found that the low variation in training examples for the NLU and failed recommendation due to the policy used were probably the main reasons for the limited performance of the system.

Dialogue State Tracking Natural Language Understanding +1

Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning

1 code implementation COLING 2022 Atsumoto Ohashi, Ryuichiro Higashinaka

When a natural language generation (NLG) component is implemented in a real-world task-oriented dialogue system, it is necessary to generate not only natural utterances as learned on training data but also utterances adapted to the dialogue environment (e. g., noise from environmental sounds) and the user (e. g., users with low levels of understanding ability).

Natural Language Understanding reinforcement-learning +4

Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning

1 code implementation SIGDIAL (ACL) 2022 Atsumoto Ohashi, Ryuichiro Higashinaka

Many studies have proposed methods for optimizing the dialogue performance of an entire pipeline task-oriented dialogue system by jointly training modules in the system using reinforcement learning.

reinforcement-learning reinforcement Learning +1

Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs

no code implementations LREC 2020 Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi, Hidetoshi Kawabata

This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data.

Question Answering

Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling

no code implementations COLING 2018 Ryo Masumura, Tomohiro Tanaka, Ryuichiro Higashinaka, Hirokazu Masataki, Yushi Aono

In addition, in order to effectively transfer knowledge between different task data sets and different language data sets, this paper proposes a partially-shared modeling method that possesses both shared components and components specific to individual data sets.

Classification Feature Engineering +3

Neural Dialogue Context Online End-of-Turn Detection

no code implementations WS 2018 Ryo Masumura, Tomohiro Tanaka, Atsushi Ando, Ryo Ishii, Ryuichiro Higashinaka, Yushi Aono

This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker{'}s utterances and collocutor{'}s utterances.

Action Detection Spoken Dialogue Systems

Investigating the Effect of Conveying Understanding Results in Chat-Oriented Dialogue Systems

no code implementations IJCNLP 2017 Koh Mitsuda, Ryuichiro Higashinaka, Junji Tomita

In this paper, we explored the effect of conveying understanding results of user utterances in a chat-oriented dialogue system by an experiment using human subjects.

Extraction of Daily Changing Words for Question Answering

no code implementations LREC 2014 Kugatsu Sadamitsu, Ryuichiro Higashinaka, Yoshihiro Matsuo

This paper proposes a method for extracting Daily Changing Words (DCWs), words that indicate which questions are real-time dependent.

BIG-bench Machine Learning Question Answering +1

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