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Creating Dynamic Robot Utterances in Human-Robot Social Interaction: Comparison of a Selection-Based Approach and a Neural Network Approach on Giving Robot Responses in Conversations
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This study examines two different approaches to dialogue management system in order to achieve dynamic utterances in human-robot social interactions. This was done in order to determine whether Robot Assisted Language Learning is viable as a solution to the current teacher shortage situation in Sweden. One approach is a selection-based approach with the use of a dialogue tree with sentence embedding while the other approach is a neural network approach with two different models; transformer and seq2seq. Results from the user testing show that both implementations were not particulary successful, although the selection-based method performed better than neural network approaches and shows promise for future research. Due to the results not reaching a satisfying level of performance and the existence of cheaper virtual education tools RALL might not be the most appropriate solution for current day Sweden but is promising as a long-term solution to the continuing trend of teacher declination and increasing labor costs.

Abstract [sv]

Denna studie undersöker två olika metoder för dialoghanteringssystem för att uppnå dynamiska yttrande i mänskliga-robot sociala interaktioner. Detta gjordes för att avgöra om Robot Assisted Language Learning (RALL) är lämplig som en lösning till den nuvarande situation i Sverige angående lärarbrist inom SFI. Ett tillvägagångssätt som tagits är ett urvalsbaserad med användandet av ett dialogträd med sentence embedding medan det andra tillvägagångssättet är genom Neural Network där två olika modeller tagits fram; en transformer modell och en seq2seq modell. Resultat från användartest visar att båda implementationerna inte var särskilt framgångsrika. Dock utfördes den urvalsbaserade metoden bättre än Neural Network metoder och är lovande för framtida forskning. På grund av att resultaten inte nått en tillfredsställande prestationsnivå och förekomsten av billigare virtuella utbildningsverktyg kanske inte är RALL den mest lämpliga lösningen för den nuvarande situationen i Sverige gällande lärarbrist inom SFI, men visar potential som en långsiktig lösning.

Place, publisher, year, edition, pages
2019. , p. 12
Series
TRITA-EECS-EX ; 2019:289
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-262208OAI: oai:DiVA.org:kth-262208DiVA, id: diva2:1360879
Examiners
Available from: 2019-11-07 Created: 2019-10-14 Last updated: 2019-11-07Bibliographically approved

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