@inproceedings{saito-etal-2018-commonsense,
title = "Commonsense Knowledge Base Completion and Generation",
author = "Saito, Itsumi and
Nishida, Kyosuke and
Asano, Hisako and
Tomita, Junji",
editor = "Korhonen, Anna and
Titov, Ivan",
booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K18-1014",
doi = "10.18653/v1/K18-1014",
pages = "141--150",
abstract = "This study focuses on acquisition of commonsense knowledge. A previous study proposed a commonsense knowledge base completion (CKB completion) method that predicts a confidence score of for triplet-style knowledge for improving the coverage of CKBs. To improve the accuracy of CKB completion and expand the size of CKBs, we formulate a new commonsense knowledge base generation task (CKB generation) and propose a joint learning method that incorporates both CKB completion and CKB generation. Experimental results show that the joint learning method improved completion accuracy and the generation model created reasonable knowledge. Our generation model could also be used to augment data and improve the accuracy of completion.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="saito-etal-2018-commonsense">
<titleInfo>
<title>Commonsense Knowledge Base Completion and Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Itsumi</namePart>
<namePart type="family">Saito</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kyosuke</namePart>
<namePart type="family">Nishida</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hisako</namePart>
<namePart type="family">Asano</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Junji</namePart>
<namePart type="family">Tomita</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 22nd Conference on Computational Natural Language Learning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Anna</namePart>
<namePart type="family">Korhonen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ivan</namePart>
<namePart type="family">Titov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Brussels, Belgium</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This study focuses on acquisition of commonsense knowledge. A previous study proposed a commonsense knowledge base completion (CKB completion) method that predicts a confidence score of for triplet-style knowledge for improving the coverage of CKBs. To improve the accuracy of CKB completion and expand the size of CKBs, we formulate a new commonsense knowledge base generation task (CKB generation) and propose a joint learning method that incorporates both CKB completion and CKB generation. Experimental results show that the joint learning method improved completion accuracy and the generation model created reasonable knowledge. Our generation model could also be used to augment data and improve the accuracy of completion.</abstract>
<identifier type="citekey">saito-etal-2018-commonsense</identifier>
<identifier type="doi">10.18653/v1/K18-1014</identifier>
<location>
<url>https://aclanthology.org/K18-1014</url>
</location>
<part>
<date>2018-10</date>
<extent unit="page">
<start>141</start>
<end>150</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Commonsense Knowledge Base Completion and Generation
%A Saito, Itsumi
%A Nishida, Kyosuke
%A Asano, Hisako
%A Tomita, Junji
%Y Korhonen, Anna
%Y Titov, Ivan
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F saito-etal-2018-commonsense
%X This study focuses on acquisition of commonsense knowledge. A previous study proposed a commonsense knowledge base completion (CKB completion) method that predicts a confidence score of for triplet-style knowledge for improving the coverage of CKBs. To improve the accuracy of CKB completion and expand the size of CKBs, we formulate a new commonsense knowledge base generation task (CKB generation) and propose a joint learning method that incorporates both CKB completion and CKB generation. Experimental results show that the joint learning method improved completion accuracy and the generation model created reasonable knowledge. Our generation model could also be used to augment data and improve the accuracy of completion.
%R 10.18653/v1/K18-1014
%U https://aclanthology.org/K18-1014
%U https://doi.org/10.18653/v1/K18-1014
%P 141-150
Markdown (Informal)
[Commonsense Knowledge Base Completion and Generation](https://aclanthology.org/K18-1014) (Saito et al., CoNLL 2018)
ACL
- Itsumi Saito, Kyosuke Nishida, Hisako Asano, and Junji Tomita. 2018. Commonsense Knowledge Base Completion and Generation. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 141–150, Brussels, Belgium. Association for Computational Linguistics.