Multilingual Knowledge Discovery in Digital Cultural Collections

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This year we focused on improving the method of cross-lingual entity matching and collecting datasets for machine translation evaluation.First, we proposed a novel method to identify records that refer to the same Japanese artwork entity in Japanese and English data sources. Our approach considered an entity as a sequence of attributes and employed a multilingual BERT-based network to enable cross-lingual entities to be compared without aligning the schema. In addition, we collected datasets and conducted further experiments to evaluate machine translations on translating ukiyo-e metadata records, especially the genre of bijin-e. In another work, we have investigated and evaluated the current state-of-the-art models to automatically discover entities and relations in short texts.