A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering. / Qiu, Chen; Zhou, Guangyou; Cai, Zhihua; Sogaard, Anders.

In: IEEE Transactions on Artificial Intelligence, Vol. 2, No. 2, 2021, p. 200-212.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Qiu, C, Zhou, G, Cai, Z & Sogaard, A 2021, 'A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering', IEEE Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 200-212. https://doi.org/10.1109/TAI.2021.3068697

APA

Qiu, C., Zhou, G., Cai, Z., & Sogaard, A. (2021). A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering. IEEE Transactions on Artificial Intelligence, 2(2), 200-212. https://doi.org/10.1109/TAI.2021.3068697

Vancouver

Qiu C, Zhou G, Cai Z, Sogaard A. A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering. IEEE Transactions on Artificial Intelligence. 2021;2(2):200-212. https://doi.org/10.1109/TAI.2021.3068697

Author

Qiu, Chen ; Zhou, Guangyou ; Cai, Zhihua ; Sogaard, Anders. / A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering. In: IEEE Transactions on Artificial Intelligence. 2021 ; Vol. 2, No. 2. pp. 200-212.

Bibtex

@article{12af7927bf7c47c786481d10f1e88b5d,
title = "A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering",
abstract = "Knowledge-based question answering (KBQA) is an essential but challenging task for artificial intelligence and natural language processing. A key challenge pertains to the design of effective algorithms for relation detection. Conventional methods model questions and candidate relations separately through the knowledge bases (KBs) without considering the rich word-level interactions between them. This approach may result in local optimal results. This article presents a global–local attentive relation detection model (GLAR) that utilizes the local module to learn the features of word-level interactions and employs the global module to acquire nonlinear relationships between questions and their candidate relations located in KBs. This article also reports on the application of an end-to-end retrieval-based KBQA system incorporating the proposed relation detection model. Experimental results obtained on two datasets demonstrated GLAR's remarkable performance in the relation detection tas...",
author = "Chen Qiu and Guangyou Zhou and Zhihua Cai and Anders Sogaard",
year = "2021",
doi = "10.1109/TAI.2021.3068697",
language = "English",
volume = "2",
pages = "200--212",
journal = "IEEE Transactions on Artificial Intelligence",
issn = "2691-4581",
publisher = "Institute of Electrical and Electronics Engineers",
number = "2",

}

RIS

TY - JOUR

T1 - A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering

AU - Qiu, Chen

AU - Zhou, Guangyou

AU - Cai, Zhihua

AU - Sogaard, Anders

PY - 2021

Y1 - 2021

N2 - Knowledge-based question answering (KBQA) is an essential but challenging task for artificial intelligence and natural language processing. A key challenge pertains to the design of effective algorithms for relation detection. Conventional methods model questions and candidate relations separately through the knowledge bases (KBs) without considering the rich word-level interactions between them. This approach may result in local optimal results. This article presents a global–local attentive relation detection model (GLAR) that utilizes the local module to learn the features of word-level interactions and employs the global module to acquire nonlinear relationships between questions and their candidate relations located in KBs. This article also reports on the application of an end-to-end retrieval-based KBQA system incorporating the proposed relation detection model. Experimental results obtained on two datasets demonstrated GLAR's remarkable performance in the relation detection tas...

AB - Knowledge-based question answering (KBQA) is an essential but challenging task for artificial intelligence and natural language processing. A key challenge pertains to the design of effective algorithms for relation detection. Conventional methods model questions and candidate relations separately through the knowledge bases (KBs) without considering the rich word-level interactions between them. This approach may result in local optimal results. This article presents a global–local attentive relation detection model (GLAR) that utilizes the local module to learn the features of word-level interactions and employs the global module to acquire nonlinear relationships between questions and their candidate relations located in KBs. This article also reports on the application of an end-to-end retrieval-based KBQA system incorporating the proposed relation detection model. Experimental results obtained on two datasets demonstrated GLAR's remarkable performance in the relation detection tas...

U2 - 10.1109/TAI.2021.3068697

DO - 10.1109/TAI.2021.3068697

M3 - Journal article

VL - 2

SP - 200

EP - 212

JO - IEEE Transactions on Artificial Intelligence

JF - IEEE Transactions on Artificial Intelligence

SN - 2691-4581

IS - 2

ER -

ID: 300671974