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        <title>Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion</title>
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        <namePart type="given">Naoki</namePart>
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    <name type="personal">
        <namePart type="given">Hirokazu</namePart>
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    <name type="personal">
        <namePart type="given">Daisuke</namePart>
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    <name type="personal">
        <namePart type="given">Sadao</namePart>
        <namePart type="family">Kurohashi</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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    <abstract>Considerable effort has been devoted to building commonsense knowledge bases. However, they are not available in many languages because the construction of KBs is expensive. To bridge the gap between languages, this paper addresses the problem of projecting the knowledge in English, a resource-rich language, into other languages, where the main challenge lies in projection ambiguity. This ambiguity is partially solved by machine translation and target-side knowledge base completion, but neither of them is adequately reliable by itself. We show their combination can project English commonsense knowledge into Japanese and Chinese with high precision. Our method also achieves a top-10 accuracy of 90% on the crowdsourced English–Japanese benchmark. Furthermore, we use our method to obtain 18,747 facts of accurate Japanese commonsense within a very short period.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion
%A Otani, Naoki
%A Kiyomaru, Hirokazu
%A Kawahara, Daisuke
%A Kurohashi, Sadao
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F otani-etal-2018-cross
%X Considerable effort has been devoted to building commonsense knowledge bases. However, they are not available in many languages because the construction of KBs is expensive. To bridge the gap between languages, this paper addresses the problem of projecting the knowledge in English, a resource-rich language, into other languages, where the main challenge lies in projection ambiguity. This ambiguity is partially solved by machine translation and target-side knowledge base completion, but neither of them is adequately reliable by itself. We show their combination can project English commonsense knowledge into Japanese and Chinese with high precision. Our method also achieves a top-10 accuracy of 90% on the crowdsourced English–Japanese benchmark. Furthermore, we use our method to obtain 18,747 facts of accurate Japanese commonsense within a very short period.
%U https://aclanthology.org/C18-1128
%P 1508-1520

Markdown (Informal)

[Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion](https://aclanthology.org/C18-1128) (Otani et al., COLING 2018)

• Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion (Otani et al., COLING 2018)

ACL

• Naoki Otani, Hirokazu Kiyomaru, Daisuke Kawahara, and Sadao Kurohashi. 2018. Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1508–1520, Santa Fe, New Mexico, USA. Association for Computational Linguistics.