Contextual Reactive Pattern on Chatbot building Platforms
TL;DR: The effects of using a platform to build a chatbot is discussed and the Contextual Reactive pattern used for chatbot definition is discussed, which describes the "reaction" that must take place when the context is observed.
Abstract: Building a chatbot with an iterative development process poses certain challenges for the chatbot developer. The developer is expected to produce a deployable version of the chatbot at the end of a short development cycle. Every iteration should incrementally increase the capability of the chatbot and implement a subset of overall user stories based upon a priority list, similar to any other project developed using iterative development. In this regard, commercial chatbot-building platforms offer multiple advantages to the chatbot developer, provided that the developer can map these user stories in a particular form. To do so, for every query the chatbot is expected to answer, the developer must evaluate the intention of the user. Based on the intention, the query must be processed differently, which may involve execution of some business logic. In addition, the processing of the query may require specific data items which the user must supply as part of the conversation with the chatbot. Thus, the chatbot is defined by supplying a "context" that it may encounter, and the "reaction" that must take place when the context is observed. In this work, we discuss the effects of using a platform to build a chatbot and discuss the Contextual Reactive pattern used for chatbot definition.
Citations
More filters
01 Sep 2020
TL;DR: Based on the experiences with three popular chatbot-building platforms - Google Dialogflow, IBM Watson Assistant and Amazon Lex, a list of desirable features that these platforms should exhibit in order to cater to their mixed user base is presented.
Abstract: There is a visible eagerness in the business community to integrate chatbots with their websites and mobile apps. They provide a humanised interface to information and can serve as digital assistants that can perform tasks on behalf of an individual. There are many commercial platforms which provide interfaces to build these chatbots. They are used by both professional software developers as well as people from non-IT backgrounds. Based on our experiences with three popular chatbot-building platforms - Google Dialogflow, IBM Watson Assistant and Amazon Lex, we present a list of desirable features that these platforms should exhibit in order to cater to their mixed user base. We also rate the availability and ease of use of these features on the current versions of these platforms.
9 citations
Cites background or methods from "Contextual Reactive Pattern on Chat..."
...We term this pattern as theContextual Reactivepattern [4]....
[...]
...We suggest that the users familiarize themselves with the development paradigm, including the Contextual Reactive pattern [4] for de ning the chatbots....
[...]
01 Dec 2022
TL;DR: In this paper , a Natural Language Processing (NLP) assisted virtual course assistant solution was proposed to support online teaching and learning in the context of the COVID-19 pandemic.
Abstract: In this paper, we present a Natural Language Processing (NLP)-empowered virtual course assistant solution that supports online teaching and learning in the context of the COVID-19 pandemic. We leverage advanced technologies of pre-trained language models in NLP to construct several fundamental functionalities for the virtual course assistant. The assistant is designed to answer general course enquiries to reduce time-consuming and repeated human responses, to answer course-related knowledge questions by understanding both queries and teaching materials, and to analyze students' feedback via sentiment analysis. Additionally, we have constructed the course-related database and cross-platform virtual assistants for both website and mobile applications. Different pre-trained models are utilized to fine-tune the dataset in each type of model. By comparing different datasets and analyzing their performance, the best performance model is selected for the virtual assistant. Empirically, adopting NLP-empowered virtual course assistants in class improves teaching and learning experiences: With the help of an NLP-empowered virtual course assistant, the teaching team could devote more effort and time to answering complex questions; For students, an immediate response increases their motivation to study. Thus, the online system could give an excellent user experience to a wide variety of users. Our code and dataset are released at https://github.com/Heriannan/NLP-for-educationVirtualAssistant.
01 Dec 2022
TL;DR: In this article , a Natural Language Processing (NLP) assisted virtual course assistant solution was proposed to support online teaching and learning in the context of the COVID-19 pandemic.
Abstract: In this paper, we present a Natural Language Processing (NLP)-empowered virtual course assistant solution that supports online teaching and learning in the context of the COVID-19 pandemic. We leverage advanced technologies of pre-trained language models in NLP to construct several fundamental functionalities for the virtual course assistant. The assistant is designed to answer general course enquiries to reduce time-consuming and repeated human responses, to answer course-related knowledge questions by understanding both queries and teaching materials, and to analyze students' feedback via sentiment analysis. Additionally, we have constructed the course-related database and cross-platform virtual assistants for both website and mobile applications. Different pre-trained models are utilized to fine-tune the dataset in each type of model. By comparing different datasets and analyzing their performance, the best performance model is selected for the virtual assistant. Empirically, adopting NLP-empowered virtual course assistants in class improves teaching and learning experiences: With the help of an NLP-empowered virtual course assistant, the teaching team could devote more effort and time to answering complex questions; For students, an immediate response increases their motivation to study. Thus, the online system could give an excellent user experience to a wide variety of users. Our code and dataset are released at https://github.com/Heriannan/NLP-for-educationVirtualAssistant.
01 Jan 2022
TL;DR: In this paper, the authors describe the design, development, and testing of a question-answering chatbot to inform COVID-19 at Cali town from March to December 2020.
Abstract: This paper aims to describe the design, development, and testing of a question-answering chatbot to inform COVID-19 at Cali town. The chatbot is based on the model of natural language processing, and it is capable of holding a question-and-answer conversation about the pandemic. This document presents the sources of information to solve information needs in the Cali town's risk scenery from March to December 2020; Based on the sources of information, a corpus with 636 sentences was built. Three models were trained bases on the corpus. The models were trained incremental prototyping: A baseline model that responds to general questions, cases, active cases, and deaths at a geographic point of an area or region of interest (BC), the baseline model, zones and news, decrees or regulations generated by the Government during the risk situation (BCN) and the final model that responds the previous items and to frequently asked questions (BFAQ). A satisfaction survey of the prototype was developed to evaluate the chatbot, and the models were evaluated by metrics of PLN precision, coverage, and F-measure. The analysis and results showed that the final model (BFAQS) showed values upper 88% in the three measurements., besides, the BFACS held 1480 conversations, with an average conversation engagement of 4.12 minutes. Furthermore, the survey results show that 87% would use the chatbot again to obtain information about COVID-19.