ToD4IR: A Humanised Task-Oriented Dialogue System for Industrial Robots

Li, Chen; Zhang, Xiaochun; Chrysostomou, Dimitrios; Yang, Hongji

doi:10.1109/access.2022.3202554

Cited by 18 publications

(10 citation statements)

References 41 publications

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“…Furthermore, inspired by [50], Anchor, Reveal and Encourage (ARE) principles are introduced to assist the wizard for small talk responses if it is desired. Table 1 explains the conversation strategies and small talk principles with examples [51].…”

Section: ) Conversation Strategymentioning

confidence: 99%

IRWoZ: Constructing an Industrial Robot Wizard-of-OZ Dialoguing Dataset

Chrysostomou

Zhang

et al. 2023

IEEE Access

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Enabling a flexible and natural human-robot interaction (HRI) for industrial robots is a critical yet challenging task that can be facilitated by the use of conversational artificial intelligence (AI). Prior research has concentrated on strengthening interactions through the deployment of social robots, while disregarding the capabilities required to boost the flexibility and user experience associated with human-robot collaboration (HRC) on manufacturing tasks. One of the main challenges is the lack of publicly available industrial-oriented dialogue datasets for the training of conversational AI. In this work, we present an industrial robot wizard-of-Oz dialoguing dataste (IRWoZ) focused on enabling HRC in manufacturing tasks. The dataset covers four domains: assembly, transportation, position, and relocation. It is created using the Wizard-of-Oz technique to be less noisy. We manually constructed, annotated and validated dialogue segments (e.g., intentions, slots, annotations), as well as the responses. Building upon the proposed dataset, we benchmark it on the state-of-the-art (SoTA) language models, generative pretrained (GPT-2) models, on dialogue state tracking and response generation tasks. We expect that the IRWoZ dataset will facilitate exciting ongoing dialogue research and we provide it freely accessible at https://github.com/lcroy/ToD4IR/tree/main/dataset.INDEX TERMS data collection, data annotation, dialogue systems, virtual assistants, human-robot interaction.

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Section: ) Conversation Strategymentioning

confidence: 99%

IRWoZ: Constructing an Industrial Robot Wizard-of-OZ Dialoguing Dataset

Chrysostomou

Zhang

et al. 2023

IEEE Access

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“…In the context of dialog systems, large language models play a crucial role in facilitating language understanding. The development of large pretrained models like Google's Meena and Microsoft's Blender has led to significant improvements in the naturalness and coherence of open-domain chatbots [302]. These models possess the ability to generate informative, interesting, and harmless responses, making conversational agents much more usable.…”

Section: H Dialog Systemsmentioning

confidence: 99%

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

Hadi,

tashi,

Qureshi

et al. 2023

Preprint

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<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. Large language models (LLMs) are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, and question-answering. This survey paper provides a comprehensive overview of LLMs, including their history, architecture, training methods, applications, and challenges. The paper begins by discussing the fundamental concepts of generative AI and the architecture of generative pre- trained transformers (GPT). It then provides an overview of the history of LLMs, their evolution over time, and the different training methods that have been used to train them. The paper then discusses the wide range of applications of LLMs, including medical, education, finance, and engineering. It also discusses how LLMs are shaping the future of AI and how they can be used to solve real-world problems. The paper then discusses the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, and computational resource requirements. It also highlights techniques for enhancing the robustness and controllability of LLMs, and addressing bias, fairness, and generation quality issues. Finally, the paper concludes by highlighting the future of LLM research and the challenges that need to be addressed in order to make LLMs more reliable and useful. This survey paper is intended to provide researchers, practitioners, and enthusiasts with a comprehensive understanding of LLMs, their evolution, applications, and challenges. By consolidating the state-of-the-art knowledge in the field, this survey serves as a valuable resource for further advancements in the development and utilization of LLMs for a wide range of real-world applications. The GitHub repo for this project is available at https://github.com/anas-zafar/LLM-Survey</p>

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“…In general, a task-oriented dialogue system [ 21 , 22 , 23 , 39 , 40 , 41 ] adopts one of the following architectures: a conventional pipeline system or an end-to-end neural model [ 42 ]. The pipeline scheme is a modular architecture consisting of four components: natural language understanding (NLU), dialog state tracker (DST), dialog policy (POL), and natural language generation (NLG).…”

Section: Related Work: Task-oriented Dialogue Systemsmentioning

confidence: 99%

“…Generally speaking, dialogue systems aim to generate appropriate responses to user input, and it is an important research direction not only due to its practical applications but also for its direct connection to artificial intelligence (AI) [ 16 , 17 ]. Dialogue systems can be categorized into two classes according to their applications: (1) open-domain dialogue systems that aim at synthesizing human-like conversations with users [ 18 , 19 , 20 ], and (2) task-oriented dialogue (TOD) systems of which the goal is to help human users to complete certain tasks such as virtual assistant [ 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…In the two main classes of dialogue system, task-oriented dialogue system has been the focus of the research community as it has many valuable applications such as an online booking system, counselling service, and in-vehicle assistant [ 22 , 24 ]. In recent years, significant progress has been made in the development of task-oriented dialogue systems beginning with the deployment of neural models, especially pretrained language models (PLM) [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant

Yin

Peng

et al. 2022

IJERPH

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An advanced driver simulator methodology facilitates a well-connected interaction between the environment and drivers. Multiple traffic information environment language processing aims to help drivers accommodate travel demand: safety prewarning, destination navigation, hotel/restaurant reservation, and so on. Task-oriented dialogue systems generally aim to assist human users in achieving these specific goals by a conversation in the form of natural language. The development of current neural network based dialogue systems relies on relevant datasets, such as KVRET. These datasets are generally used for training and evaluating a dialogue agent (e.g., an in-vehicle assistant). Therefore, a simulator for the human user side is necessarily required for assessing an agent system if no real person is involved. We propose a new end-to-end simulator to operate as a human driver that is capable of understanding and responding to assistant utterances. This proposed driver simulator enables one to interact with an in-vehicle assistant like a real person, and the diversity of conversations can be simply controlled by changing the assigned driver profile. Results of our experiment demonstrate that this proposed simulator achieves the best performance on all tasks compared with other models.

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ToD4IR: A Humanised Task-Oriented Dialogue System for Industrial Robots

Cited by 18 publications

References 41 publications

IRWoZ: Constructing an Industrial Robot Wizard-of-OZ Dialoguing Dataset

IRWoZ: Constructing an Industrial Robot Wizard-of-OZ Dialoguing Dataset

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant

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