Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/24486
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dc.contributor.advisorKaul, Asha-
dc.contributor.authorSingh, Tanmay-
dc.date.accessioned2021-10-27T06:44:11Z-
dc.date.available2021-10-27T06:44:11Z-
dc.date.issued2020-
dc.identifier.urihttp://hdl.handle.net/11718/24486-
dc.description.abstractChatbots are becoming increasingly common recently, and everyone is in a hurry to get their own. Organizations use them to alleviate workload and improve services. At the same time, individuals and content creators who are making a living out of the Web 2.0 and social media phenomena can utilize them for audience engagement and click-through rates. Tech-giants such as Facebook, Google, Microsoft, IBM all have their version of AI-based virtual assistants and chatbot frameworks. With the current popularity and vast scope, chatbots deserve an in-depth study of their application, implementation, and interactions to clear a path for their normalization, so they do not end up like Sony’s Betamax, the apparent superior technology that lost to the VHS videotape format, i.e., the rule-based chatbots winning over superior AI technology. In the following study, we try to sum up the different factors in building, selecting, and training a chatbot, and how users perceive them.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management Ahmedabaden_US
dc.subjectChatboten_US
dc.subjectVirtual assistanten_US
dc.subjectDeploymenten_US
dc.subjectEntitiesen_US
dc.subjectIntentsen_US
dc.subjectSlotsen_US
dc.subjectTuring testen_US
dc.subjectDiscourseen_US
dc.subjectUser-preferencesen_US
dc.titleFinding improvement areas in human-chatbot conversation experience by isolating limitations to learning and context: a quantitative and qualitative studyen_US
dc.typeStudent Projecten_US
Appears in Collections:Student Projects

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