Isa: Intuit Smart Agent, A Neural-Based Agent-Assist Chatbot

Xue, Zijun; Ko, Ting-Yu; Yuchen, Neo; Wu, Ming-Kuang Daniel; Hsieh, Chu-Cheng

doi:10.1109/icdmw.2018.00202

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…Machine learning and sentiment analysis advancements have given conversational agents the ability to respond emotionally to users. [29] [77] [43][78] [44] discussed conversational agent systems for the business domain using different machine learning and deep learning techniques. In business-like E-commerce, banking mainly conversational agents are designed to answer FAQs.…”

Section: ) Conversational Agents In Business -mentioning

confidence: 99%

AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

et al. 2022

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Artificial intelligence is changing the world, especially the interaction between machines and humans. Learning and interpreting natural languages and responding have paved the way for many technologies and applications. The amalgam of machine learning, deep learning, and natural language processing helped Conversational Artificial Intelligence (AI) to change the face of Human-Computer Interaction (HCI). A conversational agent is an excellent example of conversational AI, which imitates the natural language. This article presents a sweeping overview of conversational agents that includes different techniques such as pattern-based, machine learning, and deep learning used to implement conversational agents. It also discusses the panorama of different tasks in conversational agents. This study also focuses on how conversational agents can simulate human behavior by adding emotions, sentiments, and affect to the context. With the advancements in recent trends and the rise in deep learning models, the authors review the deep learning techniques and various publicly available datasets used in conversational agents. This article unearths the research gaps in conversational agents and gives insights into future directions.

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Section: ) Conversational Agents In Business -mentioning

confidence: 99%

AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

et al. 2022

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“…These examples are described emphasizing the data sources of contextual knowledge and the mechanisms used for context integration. Some examples include user personalization features or user preferences and user's past interactions with the agent for adaptive knowledge bases to domain-specific queries [156], user feedback regarding the users' runtime feedback and the agents' responses to re-train the generative models [178], and user historical data (e.g., user's purchase in an e-commerce website) from the software system in which the agent is integrated or from third-party services for recommender tasks [146]. Kocaballi et al [90] present an exhaustive enumeration of examples related to a list of personalized contents and context-adaptation purposes or goals.…”

Section: Context Integration Techniques (F7)mentioning

confidence: 99%

Software-Based Dialogue Systems: Survey, Taxonomy, and Challenges

Motger¹,

Franch

Marco

2022

ACM Comput. Surv.

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The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks, the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview on the current state of research of conversational agents covering all research perspectives involved is missing. Motivated by this context, this paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents’ field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces.

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“…These examples are described emphasizing the data sources of contextual knowledge and the mechanisms used for context integration. Some examples include user personalization features and user's past interactions with the agent for knowledge base adaptation to domain-specific queries [119], user historic feedback regarding the agents' responses to re-train the generative models [140], and user historical data (e.g., user's purchase in an e-commerce website) from the software system in which the agent is integrated or from third-party services for recommendations [111]. Kocaballi et al [72] present an exhaustive enumeration of examples related to a list of personalized contents and context-adaptation purposes or goals.…”

Section: Context Integration Techniques (F7)mentioning

confidence: 99%

Conversational Agents in Software Engineering: Survey, Taxonomy and Challenges

Motger¹,

Franch²,

Marco³

2021

Preprint

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The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks, the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview on the current state of research of conversational agents covering all research perspectives involved is missing. Motivated by this context, this paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents' field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces. CCS Concepts: • General and reference → Surveys and overviews; • Human-centered computing → Natural language interfaces.

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Isa: Intuit Smart Agent, A Neural-Based Agent-Assist Chatbot

Cited by 11 publications

References 3 publications

AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

Software-Based Dialogue Systems: Survey, Taxonomy, and Challenges

Conversational Agents in Software Engineering: Survey, Taxonomy and Challenges

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