QUESTION ANSWERING SYSTEM BASED ON PRE-TRAINING MODEL AND RETRIEVAL RERANKING FOR INDUSTRY 4.0

Ta Fu Chen; Yi Xing Lin; Ming Hsiang Su; Po Kai Chen; Tzu Chiang Tai; Jia Ching Wang

doi:10.1109/APSIPAASC58517.2023.10317470

QUESTION ANSWERING SYSTEM BASED ON PRE-TRAINING MODEL AND RETRIEVAL RERANKING FOR INDUSTRY 4.0

Ta Fu Chen, Yi Xing Lin, Ming Hsiang Su, Po Kai Chen, Tzu Chiang Tai, Jia Ching Wang

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

摘要

By providing enhanced knowledge retrieval capabilities, real-time decision support, and efficient information exchange, question answering (QA) systems play a crucial role in driving productivity, efficiency, and innovation in Industry 4.0. Today's most reliable knowledge-based QA systems require a large knowledge base, which tends to consume more reasoning time. In order to improve the inference speed and response accuracy of the system, this paper adds a Reranker between the Retriever and Reader of the traditional two-stage mechanism. This study uses pretrained Roberta to perform system retrieval and improve data processing and training methods. Experiments on Chinese Wikipedia show that the proposed system significantly reduces the system response time and improves the accuracy and scope of the response.

原文	???core.languages.en_GB???
主出版物標題	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	2178-2181
頁數	4
ISBN（電子）	9798350300673
DOIs	https://doi.org/10.1109/APSIPAASC58517.2023.10317470
出版狀態	已出版 - 2023
事件	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, Taiwan 持續時間: 31 10月 2023 → 3 11月 2023

出版系列

名字	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
國家/地區	Taiwan
城市	Taipei
期間	31/10/23 → 3/11/23

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10.1109/APSIPAASC58517.2023.10317470

引用此

Chen, T. F., Lin, Y. X., Su, M. H., Chen, P. K., Tai, T. C., & Wang, J. C. (2023). QUESTION ANSWERING SYSTEM BASED ON PRE-TRAINING MODEL AND RETRIEVAL RERANKING FOR INDUSTRY 4.0. 於 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 (頁 2178-2181). (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPAASC58517.2023.10317470

Chen, Ta Fu ; Lin, Yi Xing ; Su, Ming Hsiang 等. / QUESTION ANSWERING SYSTEM BASED ON PRE-TRAINING MODEL AND RETRIEVAL RERANKING FOR INDUSTRY 4.0. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. 頁 2178-2181 (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023).

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title = "QUESTION ANSWERING SYSTEM BASED ON PRE-TRAINING MODEL AND RETRIEVAL RERANKING FOR INDUSTRY 4.0",

abstract = "By providing enhanced knowledge retrieval capabilities, real-time decision support, and efficient information exchange, question answering (QA) systems play a crucial role in driving productivity, efficiency, and innovation in Industry 4.0. Today's most reliable knowledge-based QA systems require a large knowledge base, which tends to consume more reasoning time. In order to improve the inference speed and response accuracy of the system, this paper adds a Reranker between the Retriever and Reader of the traditional two-stage mechanism. This study uses pretrained Roberta to perform system retrieval and improve data processing and training methods. Experiments on Chinese Wikipedia show that the proposed system significantly reduces the system response time and improves the accuracy and scope of the response.",

keywords = "Pre-training model, QA system, Reranking",

author = "Chen, {Ta Fu} and Lin, {Yi Xing} and Su, {Ming Hsiang} and Chen, {Po Kai} and Tai, {Tzu Chiang} and Wang, {Jia Ching}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 ; Conference date: 31-10-2023 Through 03-11-2023",

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doi = "10.1109/APSIPAASC58517.2023.10317470",

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series = "2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023",

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Chen, TF, Lin, YX, Su, MH, Chen, PK, Tai, TC & Wang, JC 2023, QUESTION ANSWERING SYSTEM BASED ON PRE-TRAINING MODEL AND RETRIEVAL RERANKING FOR INDUSTRY 4.0. 於 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, Institute of Electrical and Electronics Engineers Inc., 頁 2178-2181, 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, Taipei, Taiwan, 31/10/23. https://doi.org/10.1109/APSIPAASC58517.2023.10317470

QUESTION ANSWERING SYSTEM BASED ON PRE-TRAINING MODEL AND RETRIEVAL RERANKING FOR INDUSTRY 4.0. / Chen, Ta Fu; Lin, Yi Xing; Su, Ming Hsiang 等.
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 2178-2181 (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023).

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

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T1 - QUESTION ANSWERING SYSTEM BASED ON PRE-TRAINING MODEL AND RETRIEVAL RERANKING FOR INDUSTRY 4.0

AU - Chen, Ta Fu

AU - Lin, Yi Xing

AU - Su, Ming Hsiang

AU - Chen, Po Kai

AU - Tai, Tzu Chiang

AU - Wang, Jia Ching

PY - 2023

Y1 - 2023

N2 - By providing enhanced knowledge retrieval capabilities, real-time decision support, and efficient information exchange, question answering (QA) systems play a crucial role in driving productivity, efficiency, and innovation in Industry 4.0. Today's most reliable knowledge-based QA systems require a large knowledge base, which tends to consume more reasoning time. In order to improve the inference speed and response accuracy of the system, this paper adds a Reranker between the Retriever and Reader of the traditional two-stage mechanism. This study uses pretrained Roberta to perform system retrieval and improve data processing and training methods. Experiments on Chinese Wikipedia show that the proposed system significantly reduces the system response time and improves the accuracy and scope of the response.

AB - By providing enhanced knowledge retrieval capabilities, real-time decision support, and efficient information exchange, question answering (QA) systems play a crucial role in driving productivity, efficiency, and innovation in Industry 4.0. Today's most reliable knowledge-based QA systems require a large knowledge base, which tends to consume more reasoning time. In order to improve the inference speed and response accuracy of the system, this paper adds a Reranker between the Retriever and Reader of the traditional two-stage mechanism. This study uses pretrained Roberta to perform system retrieval and improve data processing and training methods. Experiments on Chinese Wikipedia show that the proposed system significantly reduces the system response time and improves the accuracy and scope of the response.

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BT - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Y2 - 31 October 2023 through 3 November 2023

ER -

Chen TF, Lin YX, Su MH, Chen PK, Tai TC, Wang JC. QUESTION ANSWERING SYSTEM BASED ON PRE-TRAINING MODEL AND RETRIEVAL RERANKING FOR INDUSTRY 4.0. 於 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 2178-2181. (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023). doi: 10.1109/APSIPAASC58517.2023.10317470

QUESTION ANSWERING SYSTEM BASED ON PRE-TRAINING MODEL AND RETRIEVAL RERANKING FOR INDUSTRY 4.0

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