10 accepted papers
In recent years, numerous studies have been conducted on dialogue robots powered by large language models,enabling sophisticated interactions such as providing guidance and engaging in small talk. However, the interaction performance remains imperfect, and the robots sometimes cause problems during
The naturalness of responses in spoken dialogue systems has been significantly improved by the introduction of large language models (LLMs), although many challenges remain until human-like turn-taking can be achieved. A turn-taking model called Voice Activity Projection (VAP) is gaining attention b…
Post-processing networks (PPNs) are components that modify the outputs of arbitrary modules in task-oriented dialogue systems and are optimized using reinforcement learning (RL) to improve the overall task completion capability of the system. However, previous PPN-based approaches have been limited…
The potential usage scenarios of dialogue systems will be greatly expanded if they are able to collaborate more creatively with humans. Many studies have examined ways of building such systems, but most of them focus on problem-solving dialogues, and relatively little research has been done on syste…
To construct a chat-oriented dialogue system that will be used for a long time by users, it is important to build a good relationship between the user and the system. To achieve a good relationship, several methods for remembering and utilizing information on users (preferences, experiences, jobs, e…
Dialogue datasets are crucial for deep learning-based task-oriented dialogue system research. While numerous English language multi-domain task-oriented dialogue datasets have been developed and contributed to significant advancements in task-oriented dialogue systems, such a dataset does not exist…
Recently, post-processing networks (PPNs), which modify the outputs of arbitrary modules including non-differentiable ones in task-oriented dialogue systems, have been proposed. PPNs have successfully improved the dialogue performance by post-processing natural language understanding (NLU), dialogue…
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…
Creating chatbots to behave like real people is important in terms of believability. Errors in general chatbots and chatbots that follow a rough persona have been studied, but those in chatbots that behave like real people have not been thoroughly investigated. We collected a large amount of user in…
This paper describes neural ConfNet classification, a novel fully neural network based spoken utterance classification method that uses word confusion networks (ConfNets). Our motivation is to establish a spoken utterance classification method that can precisely understand natural language and robus…