Usage-Based Learning in Human Interaction With an Adaptive Virtual Assistant

Delgrange, Clément; Dussoux, Jean-Michel; Dominey, Peter Ford

doi:10.1109/tcds.2019.2927399

Cited by 8 publications

(6 citation statements)

References 40 publications

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“…This also potentially includes various forms of natural language human-computer interaction, including the Internet of Things voice interaction systems, intelligent multi-agent systems (e.g., virtual assistant/customer service/consultant, etc.) [18,59]. The progress in this field will also greatly promote the operation of human society to be more convenient, efficient, and intelligent.…”

Section: Nlp In Cyber-physical Systemsmentioning

confidence: 99%

A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

et al. 2021

ACM Trans. Internet Technol.

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Cyber-Physical Systems (CPS), as a multi-dimensional complex system that connects the physical world and the cyber world, has a strong demand for processing large amounts of heterogeneous data. These tasks also include Natural Language Inference (NLI) tasks based on text from different sources. However, the current research on natural language processing in CPS does not involve exploration in this field. Therefore, this study proposes a Siamese Network structure that combines Stacked Residual Long Short-Term Memory (bidirectional) with the Attention mechanism and Capsule Network for the NLI module in CPS, which is used to infer the relationship between text/language data from different sources. This model is mainly used to implement NLI tasks and conduct a detailed evaluation in three main NLI benchmarks as the basic semantic understanding module in CPS. Comparative experiments prove that the proposed method achieves competitive performance, has a certain generalization ability, and can balance the performance and the number of trained parameters.

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Section: Nlp In Cyber-physical Systemsmentioning

confidence: 99%

A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

et al. 2021

ACM Trans. Internet Technol.

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“…The obvious way to build such a system, would be to ask for the guidance of the user each time a request cannot be interpreted using the known patterns P [9]. This would happen in the case where many patterns match the user's request or no pattern matches it.…”

Section: The Natural Language Understanding Modulementioning

confidence: 99%

“…We integrate the AidMe algorithm to an existing virtual assistant described in [9] that learns by GUIs demonstrations and natural language instructions. The assistant is able to learn mapping of natural language orders to procedural knowledge in order to accomplish tasks on a digital environment composed of web services.…”

Section: Integration To a Digital Assistantmentioning

confidence: 99%

“…The main limitations of these assistants is their inability to adapt to their users and to learn new tasks from their interaction with arXiv:2001.06007v1 [cs.HC] 16 Jan 2020 users. The few digital assistants [1,9,19,23] that have been proposed to tackle these issues suffer from a weak comprehension capability: the assistant may experience difficulty in understanding user requests that are not expressed in the exact learned forms. As their task repertory grows and depends on the interaction they have with the user, they cannot rely on the traditional NLU tools mentioned above.…”

Section: Introductionmentioning

confidence: 99%

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User-in-the-loop adaptive intent detection for instructable digital assistant

Lair

Delgrange²,

Mugisha³

et al. 2020

Proceedings of the 25th International Conference on Intelligent User Interfaces

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People are becoming increasingly comfortable using Digital Assistants (DAs) to interact with services or connected objects. However, for non-programming users, the available possibilities for customizing their DA are limited and do not include the possibility of teaching the assistant new tasks. To make the most of the potential of DAs, users should be able to customize assistants by instructing them through Natural Language (NL). To provide such functionalities, NL interpretation in traditional assistants should be improved: (1) The intent identification system should be able to recognize new forms of known intents, and to acquire new intents as they are expressed by the user. ( 2) In order to be adaptive to novel intents, the Natural Language Understanding module should be sample efficient, and should not rely on a pretrained model. Rather, the system should continuously collect the training data as it learns new intents from the user. In this work, we propose AidMe (Adaptive Intent Detection in Multi-Domain Environments), a user-in-the-loop adaptive intent detection framework that allows the assistant to adapt to its user by learning his intents as their interaction progresses. AidMe builds its repertoire of intents and collects data to train a model of semantic similarity evaluation that can discriminate between the learned intents and autonomously discover new forms of known intents. AidMe addresses two major issues -intent learning and user adaptation -for instructable digital assistants. We demonstrate the capabilities of AidMe as a standalone system by comparing it with a one-shot learning system and a pretrained NLU module through simulations of interactions with a user. We also show how AidMe can smoothly integrate to an existing instructable digital assistant.

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“…Users can communicate with IVA systems through software applications, or, most commonly, voice activation devices. The user communication side of IVAs are developed by using Artificial Intelligence (AI) that must include Speech Recognition and Natural Language Processing techniques for the system to understand the input (human language) and execute the relevant procedures for the given commands [3,4,1]. For my case study, I am going to investigate Amazon Alexa, a popular IVA that can communicate with other IoT products and thus perform tasks for its users.…”

Section: Introductionmentioning

confidence: 99%

Virtual Assistants: A Code of Ethics

Welgemoed

2022

WFEESS

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An intelligent virtual assistant (IVA) like Amazon’s Alexa supports clients remotely by performing administrative, technological, or creative tasks. IVAs have become a part of our daily lives and collect large sums of data about their users, meaning we need a proper code of ethics to protect people’s rights to privacy as there are data leakage concerns. In this paper, I outline a code of ethics for IVAs based on research and a case study on Amazon Alexa to recognize the key principles that a code of ethics for IVAs requires to protect its users. This code of ethics can be used for development of new IVAs or updating of existing ones in order for them to follow improved ethics principles.

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Usage-Based Learning in Human Interaction With an Adaptive Virtual Assistant

Cited by 8 publications

References 40 publications

A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

User-in-the-loop adaptive intent detection for instructable digital assistant

Virtual Assistants: A Code of Ethics

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