@inproceedings{sugiura-etal-2023-building,
title = "Building a Buzzer-quiz Answering System",
author = "Sugiura, Naoya and
Yamada, Kosuke and
Sasano, Ryohei and
Takeda, Koichi and
Toyama, Katsuhiko",
editor = "Padmakumar, Vishakh and
Vallejo, Gisela and
Fu, Yao",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-srw.29",
doi = "10.18653/v1/2023.acl-srw.29",
pages = "194--199",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Building a Buzzer-quiz Answering System
%A Sugiura, Naoya
%A Yamada, Kosuke
%A Sasano, Ryohei
%A Takeda, Koichi
%A Toyama, Katsuhiko
%Y Padmakumar, Vishakh
%Y Vallejo, Gisela
%Y Fu, Yao
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sugiura-etal-2023-building
%X 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.
%R 10.18653/v1/2023.acl-srw.29
%U https://aclanthology.org/2023.acl-srw.29
%U https://doi.org/10.18653/v1/2023.acl-srw.29
%P 194-199
Markdown (Informal)
[Building a Buzzer-quiz Answering System](https://aclanthology.org/2023.acl-srw.29) (Sugiura et al., ACL 2023)
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.