Building a Buzzer-quiz Answering System

Naoya Sugiura, Kosuke Yamada, Ryohei Sasano, Koichi Takeda, Katsuhiko Toyama

Abstract
A buzzer quiz is a genre of quiz in which multiple players simultaneously listen to a quiz being read aloud and respond it by buzzing in as soon as they can predict the answer. Because incorrect answers often result in penalties, a buzzer-quiz answering system must not only predict the answer from only part of a question but also estimate the predicted answer’s accuracy. In this paper, we introduce two types of buzzer-quiz answering systems: (1) a system that directly generates an answer from part of a question by using an autoregressive language model; and (2) a system that first reconstructs the entire question by using an autoregressive language model and then determines the answer according to the reconstructed question. We then propose a method to estimate the accuracy of the answers for each system by using the internal scores of each model.
Anthology ID:
2023.acl-srw.29
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Vishakh Padmakumar, Gisela Vallejo, Yao Fu
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
194–199
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2023.acl-srw.29/
DOI:
10.18653/v1/2023.acl-srw.29
Bibkey:
Cite (ACL):
Naoya Sugiura, Kosuke Yamada, Ryohei Sasano, Koichi Takeda, and Katsuhiko Toyama. 2023. Building a Buzzer-quiz Answering System. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 194–199, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Building a Buzzer-quiz Answering System (Sugiura et al., ACL 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2023.acl-srw.29.pdf
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