Adapting a Conversational Text Generator for Online Chatbot Messaging

Klopfenstein, Lorenz Cuno; Delpriori, Saverio; Alessio, Ricci

doi:10.1007/978-3-030-17705-8_8

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…Beyond familiar uses such as configuration or NoSQL languages (e.g. MongoDB), they are used in domains as varied as statistical analysis [45], web development [5], narrative [15] and game generation [20], chatbots [52], dance [83], and fabrication [106]. We are interested in building a better understanding of JSON-style DSLs precisely because they are so prevalent-although their sudden prominence (Fig.…”

Section: Domain Specific Languagesmentioning

confidence: 99%

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

McNutt¹

2022

Preprint

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There has been substantial growth in the use of JSON-based grammars, as well as other standard data serialization languages, to create visualizations. Each of these grammars serves a purpose: some focus on particular computational tasks (such as animation), some are concerned with certain chart types (such as maps), and some target specific data domains (such as ML). Despite the prominence of this interface form, there has been little detailed analysis of the characteristics of these languages. In this study, we survey and analyze the design and implementation of 57 JSON-style DSLs for visualization. We analyze these languages supported by a collected corpus of examples for each DSL (consisting of 4395 instances) across a variety of axes organized into concerns related to domain, conceptual model, language relationships, affordances, and general practicalities. We identify tensions throughout these areas, such as between formal and colloquial specifications, among types of users, and within the composition of languages. Through this work, we seek to support language implementers by elucidating the choices, opportunities, and tradeoffs in visualization DSL design.

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Section: Domain Specific Languagesmentioning

confidence: 99%

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

McNutt¹

2022

Preprint

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“…The Bottery CA creation platform [68] consists of four components: a set of states, a blackboard-style memory, an optional set of global transitions to allow the agent to switch from state to state, and an optional grammar used by the agent to generate the final outputs of the CAs. The Bottery syntax can be simply expressed by using structured JSON and can be extended by using imperative JavaScript code.…”

Section: The Dialogue Managermentioning

confidence: 99%

Conversational Agents: Goals, Technologies, Vision and Challenges

Allouch

Azaria

Azoulay

2021

Sensors

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In recent years, conversational agents (CAs) have become ubiquitous and are a presence in our daily routines. It seems that the technology has finally ripened to advance the use of CAs in various domains, including commercial, healthcare, educational, political, industrial, and personal domains. In this study, the main areas in which CAs are successful are described along with the main technologies that enable the creation of CAs. Capable of conducting ongoing communication with humans, CAs are encountered in natural-language processing, deep learning, and technologies that integrate emotional aspects. The technologies used for the evaluation of CAs and publicly available datasets are outlined. In addition, several areas for future research are identified to address moral and security issues, given the current state of CA-related technological developments. The uniqueness of our review is that an overview of the concepts and building blocks of CAs is provided, and CAs are categorized according to their abilities and main application domains. In addition, the primary tools and datasets that may be useful for the development and evaluation of CAs of different categories are described. Finally, some thoughts and directions for future research are provided, and domains that may benefit from conversational agents are introduced.

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“…One requirement for a conversational agent to be accepted by its user is its capability to adapt to her/him. The literature on conversation adaptation is also very recent and almost limited [27,30]. Apart from Amazon Sumerian 5 , it seems that no frameworks for integrating chatbots into VR/AR applications (AD and VD areas in Figure 1) exist, which makes the RAC vision extremely novel and original.…”

Section: State Of the Artmentioning

confidence: 99%

Engineering Reliable Interactions in the Reality-Artificiality Continuum

Ancona

Bassano

Chessa

et al. 2020

Electron. Proc. Theor. Comput. Sci.

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Milgram's reality-virtuality continuum applies to interaction in the physical space dimension, going from real to virtual. However, interaction has a social dimension as well, that can go from real to artificial depending on the companion with whom the user interacts. In this paper we present our vision of the Reality-Artificiality bidimensional Continuum (RAC), we identify some challenges in its design and development and we discuss how reliable interactions might be supported inside RAC.

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Adapting a Conversational Text Generator for Online Chatbot Messaging

Cited by 6 publications

References 19 publications

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

Conversational Agents: Goals, Technologies, Vision and Challenges

Engineering Reliable Interactions in the Reality-Artificiality Continuum

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