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Found 7 papers, 4 papers with code
Influence of user personality on dialogue task performance: A case study using a rule-based dialogue system
Endowing a task-oriented dialogue system with adaptiveness to user personality can greatly help improve the performance of a dialogue task.
On the True Distribution Approximation of Minimum Bayes-Risk Decoding
Minimum Bayes-risk (MBR) decoding has recently gained renewed attention in text generation.
JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset
In this study, towards the advancement of research and development of task-oriented dialogue systems in Japanese, we constructed JMultiWOZ, the first Japanese language large-scale multi-domain task-oriented dialogue dataset.
Team Flow at DRC2023: Building Common Ground and Text-based Turn-taking in a Travel Agent Spoken Dialogue System
At the Dialogue Robot Competition 2023 (DRC2023), which was held to improve the capability of dialogue robots, our team developed a system that could build common ground and take more natural turns based on user utterance texts.
Team Flow at DRC2022: Pipeline System for Travel Destination Recommendation Task in Spoken Dialogue
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.
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning
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).
Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning
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.
