Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion

COLING 2018 · Naoki Otani, Hirokazu Kiyomaru, Daisuke Kawahara, Sadao Kurohashi ·

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.