Approaching Natural Conversation Chatbots by Interactive Dialogue Modelling &amp; Microsoft LUIS

Wachtel, Alexander; FuchB, Dominik; Schulz, Sophie; Tichy, Walter F.

doi:10.1109/cdke46621.2019.00013

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…Because of commercial opportunities, many technology giants have developed NLP platforms and opened them to the public. Examples include the IBM Watson [ 28 , 29 ], Facebook Wit.ai [ 30 ], Microsoft LUIS [ 31 , 32 ], Amazon Lex, Google Dialogflow, and so on. To have a quick development of an NLP application, developers tend to leverage the free NLP platforms.…”

Section: Related Workmentioning

confidence: 99%

DBOS: A Dialog-Based Object Query System for Hospital Nurses

Chu

Huang

2020

Sensors

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Due to the advance of indoor positioning technology, it is now possible to trace mobile medical equipment (such as electrocardiography machines, patient monitors, and so on) being moved around a hospital ward. With the support of an object tracking system, nurses can easily locate and find a device, especially when they prepare for a shift change or a medical treatment. As nurses usually face high workloads, it is highly desirable to provide nurses with a user-friendly search interface integrated into a popular mobile app that they use daily. For this, DBOS, a dialog-based object query system, is proposed, which simulates a real conversation with users via the Line messaging app’s chatbot interface. A hybrid method that combines cosine similarity (CS) and term frequency–inverse document frequency (TF-IDF) is used to determine user intent. The result is returned to the user through Line’s interface. To evaluate the applicability of DBOS, 70 search queries given by a head nurse were tested. DBOS was compared with CS, TF-IDF, and Facebook Wit.ai respectively. The experiment results show that DBOS outperforms the abovementioned methods and can achieve a 92.8% accuracy in identifying user intent.

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