ODO: Design of Multimodal Chatbot for an Experiential Media System

Bhushan, Ravi; Kulkarni, Karthik; Pandey, Vishal; Rawls, Connor; Mechtley, Brandon; Jayasuriya, Suren; Ziegler, Christian

doi:10.3390/mti4040068

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…Next, the use of chatbots in financial services and insurance was investigated (five studies), for example which three factors affect customer satisfaction with chatbots in the banking industry [83] or the use of chatbots in insurance [84]. A chatbot that recognises user perceptions via connected cameras, useful by conducting presentations [85] is an example from the sales application (five studies). Two studies examined marketing applications of chatbots; one study focused on one of the other four applications each: CRM, internal support/ITSM, innovation management, and multiple touchpoints.…”

Section: A Research Focus and Applicationsmentioning

confidence: 99%

The Use of Chatbots in Digital Business Transformation: A Systematic Literature Review

2021

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Section: A Research Focus and Applicationsmentioning

confidence: 99%

The Use of Chatbots in Digital Business Transformation: A Systematic Literature Review

2021

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“…Also, the different kinds of information used by a chatbot are generally stored in the knowledge base [3]. Recently, the advent of computer vision and mobile devices hives increased the popularity and spread of chatbots in different domains such as ecommerce, the health sector, and educational systems [4]. Also, since smart devices such as smartphones or tablet PCs have become necessities in our daily life, and lots of people are familiar with how to operate these devices, a lot of high specification smartphones have been released recently.…”

Section: Introductionmentioning

confidence: 99%

Development of a Mobile-Based Hostel Location and Recommendation Chatbot System

Isinkaye¹,

AbiodunBabs²,

Paul³

2022

IJITCS

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A Chabot is a conversational intelligent agent that has the capability of engaging in human-like interaction with its users. A lot of chatbots have been developed, but to the best of our knowledge, there are few or no chatbots that have been developed for hostel location integrated with a recommendation component to ease the cost, time, and stress of identifying suitable hostels for students, especially at higher institutions of learning. Therefore, this work develops a location-based chatbot system enhanced with recommendation capabilities to allow students to locate hostels that satisfy their needs in an easy and efficient way. The chatbot system was designed as a cross platform compatibility application with different tools and technologies which include Python, HTML and CSS with JavaScript to enhance the interactivity and attractiveness of the system. PHP provides access to MySQL database. The chatbot system provides good experience to its users in terms of loading speed, user friendliness, interface appearance, platform compatibility and recommendation accuracy as it allows them identify suitable hostel speedily and as well provides personalized recommendation of hostels to them.

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“…Today chatbots are widely used in Marketing, Education, Healthcare, and other sectors. They even are created to entertain in interactive theater performances [3]. However, this pandemic especially revealed the necessity of chatbots in the news media [4], hospitals, or the healthcare system managing huge flows of incoming questions [5].…”

Section: Introductionmentioning

confidence: 99%

Monolingual and Cross-Lingual Intent Detection without Training Data in Target Languages

2021

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Due to recent DNN advancements, many NLP problems can be effectively solved using transformer-based models and supervised data. Unfortunately, such data is not available in some languages. This research is based on assumptions that (1) training data can be obtained by the machine translating it from another language; (2) there are cross-lingual solutions that work without the training data in the target language. Consequently, in this research, we use the English dataset and solve the intent detection problem for five target languages (German, French, Lithuanian, Latvian, and Portuguese). When seeking the most accurate solutions, we investigate BERT-based word and sentence transformers together with eager learning classifiers (CNN, BERT fine-tuning, FFNN) and lazy learning approach (Cosine similarity as the memory-based method). We offer and evaluate several strategies to overcome the data scarcity problem with machine translation, cross-lingual models, and a combination of the previous two. The experimental investigation revealed the robustness of sentence transformers under various cross-lingual conditions. The accuracy equal to ~0.842 is achieved with the English dataset with completely monolingual models is considered our top-line. However, cross-lingual approaches demonstrate similar accuracy levels reaching ~0.831, ~0.829, ~0.853, ~0.831, and ~0.813 on German, French, Lithuanian, Latvian, and Portuguese languages.

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ODO: Design of Multimodal Chatbot for an Experiential Media System

Cited by 9 publications

References 28 publications

The Use of Chatbots in Digital Business Transformation: A Systematic Literature Review

The Use of Chatbots in Digital Business Transformation: A Systematic Literature Review

Development of a Mobile-Based Hostel Location and Recommendation Chatbot System

Monolingual and Cross-Lingual Intent Detection without Training Data in Target Languages

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