Chat or learn : a data-driven robust question-answering system

Luthier, Gabriel (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Popescu-Belis, Andrei (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland)

We present a voice-based conversational agent which combines the robustness of chatbots and the utility of question answering (QA) systems. Indeed, while data-driven chatbots are typically user-friendly but not goal-oriented, QA systems tend to perform poorly at chitchat. The proposed chatbot relies on a controller which performs dialogue act classification and feeds user input either to a sequence-to-sequence chatbot or to a QA system. The resulting chatbot is a spoken QA application for the Google Home smart speaker. The system is endowed with general-domain knowledge from Wikipedia articles and uses coreference resolution to detect relatedness between questions. We present our choices of data sets for training and testing the components, and present the experimental results that helped us optimize the parameters of the chatbot. In particular, we discuss the appropriateness of using the SQuAD dataset for evaluating end-to-end QA, in the light of our system’s behavior


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Publisher:
Marseille, France, 11-16 May 2020
Date:
2020-05
Marseille, France
11-16 May 2020
Pagination:
7 p.
Published in:
Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), 11–16 May 2020, Marseille, France
Numeration (vol. no.):
pp. 5474-5480
Appears in Collection:



 Record created 2020-06-23, last modified 2020-06-25

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