Human-in-the-Loop Conversation Agent for Customer Service

Paikens, Peteris; Znotiņš, Artūrs; Bārzdiņš, Guntis

doi:10.1007/978-3-030-51310-8_25

Cited by 9 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In health-care context, human-in-the-loop concepts for conversational agents supporting hospital nurse teams has proved beneficial [13]. Likewise, the notion of escalation in customer service chatbots is a practical application of the human-in-the-loop concept for robust application of chatbots in consumer service provision [83].…”

Section: State Of the Artmentioning

confidence: 99%

Future directions for chatbot research: an interdisciplinary research agenda

et al. 2021

View full text Add to dashboard Cite

Chatbots are increasingly becoming important gateways to digital services and information—taken up within domains such as customer service, health, education, and work support. However, there is only limited knowledge concerning the impact of chatbots at the individual, group, and societal level. Furthermore, a number of challenges remain to be resolved before the potential of chatbots can be fully realized. In response, chatbots have emerged as a substantial research area in recent years. To help advance knowledge in this emerging research area, we propose a research agenda in the form of future directions and challenges to be addressed by chatbot research. This proposal consolidates years of discussions at the CONVERSATIONS workshop series on chatbot research. Following a deliberative research analysis process among the workshop participants, we explore future directions within six topics of interest: (a) users and implications, (b) user experience and design, (c) frameworks and platforms, (d) chatbots for collaboration, (e) democratizing chatbots, and (f) ethics and privacy. For each of these topics, we provide a brief overview of the state of the art, discuss key research challenges, and suggest promising directions for future research. The six topics are detailed with a 5-year perspective in mind and are to be considered items of an interdisciplinary research agenda produced collaboratively by avid researchers in the field.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Future directions for chatbot research: an interdisciplinary research agenda

et al. 2021

View full text Add to dashboard Cite

show abstract

“…By detecting the specified movement of users, the system can find the optimal design scheme to reduce the human metabolic energy cost. Except for focusing on energy waste, better feedback mechanisms and interact optimiza- [182] 2014 Universal robotic control Lam & Sastry et al [183] 2014 Ergonomics Gopinath et al [184] 2016 Shared autonomy Holzinger [185] 2016 Interactive system application Conor Walsh [186] 2018 Soft wearable robots Sutton et al [187] 2018 Trustworthiness mechanism Inoue et al [188] 2019 Weakly control Mello et al [189] 2019 Ergonomics Adolfo & Yu [190] 2020 Robotic sport control Fang and Yuan [191] 2020 Ergonomics Papallas et al [192] 2020 Group control Zhou et al [193] 2020 Privacy protection Paikens et al [194] 2020 Service industry Lee et al [195] 2020 Traffic control Usman et al [196] 2020 Traffic control Cicirelli et al [197] 2020 Home automatic Cimini et al [198] 2020 Production systems Gil et al [199] 2020 Interactive system theory Abraham et al [200] 2021 Shared autonomy Ciabattoni et al [201] 2021 Trustworthiness mechanism Fosch-Villaronga et al [202] 2021 Surgery automation tion can be another issue. Lam and Sastry [183] proposed a unified framework called partially observable Markov decision process(POMDP) for modeling the feedback of three components of SHI-HITL systems named human model, the observation model and the dynamic machine model.…”

Section: Application2: System Optimizationmentioning

confidence: 99%

“…Except for the application scenes of SHI-HITL system summarized above, the system has also been utilized in the service industry [194], traffic control [195,196], home automatic [197], production systems [198] as well as other interaction systems [199,179,203].The SHI-HITL system will be continuously studied, and new sensors, control algorithms and interaction mechanisms will be applied in this field. Besides, as a trend for decreasing the human work intensity and using human supervision and decision-making in the most important places, more detailed human role division will become another focus of system construction.…”

Section: Application2: System Optimizationmentioning

confidence: 99%

A Survey of Human-in-the-loop for Machine Learning

Wu,

Xiao,

Sun

et al. 2021

Preprint

View full text Add to dashboard Cite

Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish some tasks that are hard for computers in the pipeline with the help of machine-based approaches. In this paper, we survey existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the work of improving model performance through interventional model training, and (3) the design of the system independent human-in-the-loop. Using the above categorization, we summarize major approaches in the field, along with their technical strengths/ weaknesses, we have simple classification and discussion in natural language processing, computer vision, and others. Besides, we provide some open challenges and opportunities. This survey intends to provide a high-level summarization for human-in-the-loop and motivates interested readers to consider approaches for designing effective human-in-the-loop solutions.

show abstract

“…The neural network model is based on Latvian BERT word embeddings (Znotins and Barzdins, 2020). To support entity classes of a particular domain, the NER model is trained on a larger generaldomain dataset (Gruzitis et al, 2018;Paikens et al, 2020) and a smaller domain-specific dataset. The combined model allows to recognize not only commonly used entity classes like persons, locations and organizations, but also domain specific entities like job positions and working hours.…”

Section: Entity Databasementioning

confidence: 99%

Domain Expert Platform for Goal-Oriented Dialog Collection

Gosko

Znotiņš

Skadiņa

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrati

View full text Add to dashboard Cite

Today, most dialogue systems are fully or partly built using neural network architectures. A crucial prerequisite for the creation of a goaloriented neural network dialogue system is a dataset that represents typical dialogue scenarios and includes various semantic annotations, e.g. intents, slots and dialogue actions, that are necessary for training a particular neural network architecture. In this demonstration paper, we present an easy to use interface and its backend which is oriented to domain experts for the collection of goal-oriented dialogue samples. The platform not only allows to collect or write sample dialogues in a structured way, but also provides a means for simple annotation and interpretation of the dialogues. The platform itself is language-independent; it depends only on the availability of particular language processing components for a specific language. It is currently being used to collect dialogue samples in Latvian (a highly inflected language) which represent typical communication between students and the student service.

show abstract

Human-in-the-Loop Conversation Agent for Customer Service

Cited by 9 publications

References 16 publications

Future directions for chatbot research: an interdisciplinary research agenda

Future directions for chatbot research: an interdisciplinary research agenda

A Survey of Human-in-the-loop for Machine Learning

Domain Expert Platform for Goal-Oriented Dialog Collection

Contact Info

Product

Resources

About